# skie.io, full text > Complete readable text of skie.io, generated from the published pages at > build time. See https://skie.io/llms.txt for the short index. > English only; the same pages exist under /es/ and /pt-br/. > Pages: 34. Generated from the build that produced this deploy. --- # About skie.io: The Self-Funding Cloud | skie.io URL: https://skie.io/about-us/ Description: skie.io connects cloud cost optimization, managed operations and intelligent automation into one model, so the savings fund better cloud operations. ## Cloud should create leverage, not operational drag. We help organizations reduce operational drag, improve cloud efficiency, and create more room for innovation. The Self-Funding Cloud ### One connected operating model, designed to compound. Each part funds, enables, or amplifies the next. Optimization creates savings. Savings fund operations. Operations create the conditions for automation. And the model keeps reinforcing itself. Why we exist ### Cloud became harder to manage than it should be. We kept seeing the same set of compounding pressures across organizations of every size. skie.io was built to make those pressures dissolve, not paper over them. #### Rising cloud costs Cloud spend rises faster than business value. Unused resources, untagged spend, and over-provisioned infrastructure drain budgets with no clear path to control. Cloud spend often grows faster than visibility and control. #### Operational overload Day-to-day monitoring, incidents, patching, and tickets consume skilled engineers. Too much time on maintenance, too little on innovation. The work is constant; the engineering bandwidth isn't. #### Fragmented tooling A patchwork of FinOps, monitoring, ticketing, and security tools creates blind spots, double work, and integration debt across teams. Visibility and action live in different places. #### Manual workflows High-friction workflows stay manual. Critical knowledge is fragmented across systems. Traditional automation breaks on real-world exceptions. Repetitive tasks crowd out higher-value work. #### Limited engineering Specialized cloud, FinOps, and AI talent is scarce and expensive to retain. Internal teams can't keep pace with platform demands. Specialized expertise is scarce and slow to hire. #### No unified model Optimization, operations, and AI are treated as separate budget lines. No flywheel. Savings don't compound. Cloud never pays for itself. Savings don't compound when efforts stay in silos. What we believe ### A few principles have stayed constant. As skie.io has grown, the way we work hasn't changed. These four ideas guide how we build, how we partner with customers, and how we measure whether we're doing our job well. 01 #### Clarity over complexity Cloud environments are complex enough. Our job is to make decisions clearer, operations simpler, and outcomes easier to measure, not to add another layer customers have to manage. 02 #### Engineering-led, not account-led Customers work with cloud, FinOps, and automation engineers: the people who actually understand the operational reality behind every recommendation. Not account managers reading from scripts. 03 #### Compounding, not one-time Cloud value should grow over time. Optimization savings fund better operations. Stronger operations enable smarter automation. Each engagement should be worth more in year two than it was in year one. 04 #### Trust through transparency Customer data stays in customer accounts. Recommendations come with the reasoning behind them. Workloads run where customers can see them. Trust is earned through how we operate, not how we market. From principle to practice ### How that point of view became the way we work. Our beliefs about cloud management didn't arrive all at once. Each capability (optimization, operations, automation) emerged from a real customer need, and over time we built them to reinforce each other rather than stand alone. The result is a connected model, not a stack of services. 01 #### Cloud optimization The starting point: helping organizations cut unnecessary cloud spend and use what they have more effectively. Visibility into waste, smarter resource decisions, and continuous tuning: the foundation everything else builds on. 02 #### Managed operations As environments grew more complex, customers needed more than recommendations. They needed operational support. Day-to-day management with stronger governance, better reliability, and less burden on internal teams. 03 #### Intelligent automation Operational scale exposed a new bottleneck: high-friction workflows that slow execution and tie up skilled people. AI-driven automation applied where it matters most: resolving exceptions, accelerating decisions, improving quality. 04 #### Connected model The three capabilities work better together than apart. Optimization funds operations. Operations create the conditions for automation. Automation compounds the value of both. One connected model, designed so each part reinforces the others. How we put this into practice ### One operating model, three reinforcing parts. Each addresses a real customer problem. Together, they make cloud easier to manage and the value compounds over time. #### Sage Cloud efficiency Reduce waste and improve cloud economics through intelligent right-sizing, commitment optimization, and forecasting. #### Guardian Operations without the drag Run cloud operations with proactive monitoring, governance, and engineering oversight, so internal teams can focus elsewhere. #### Muse Intelligent automation Take on the manual, exception-heavy work across ERP, ITSM, HR, and procurement using AI agents that act on real context. Leadership ### The people building skie.io. The founders, executives, and platform engineers behind skie.io. #### Sal Jamil Founder & CEO #### Dean Adamopoulos Founder & President #### Rachad Honein CTO #### Rogério Gonçalves SVP Platform Engineering #### Manos Papakonstantinou VP Cloud Services #### Ernesto Leyva Freundt CMO #### Alfredo Loyola Service Delivery Director, LATAM #### Alec Griffiths Global Sage Product Owner #### Danilo Acquaviva Lead Platform Engineer #### Francisco Freire Lead Platform Engineer #### Lenon Oliveira Lead Platform Engineer Global Delivery ### Engineering and operations around the world. A globally distributed team delivering optimization, managed services, and AI automation across the Americas and Europe. United States Fort Lauderdale, FL · Charlotte, NC HQ Brazil São Paulo LATAM Peru Lima LATAM Portugal Lisbon EMEA USA Peru Brazil Portugal Ready to start? ### Want to see how this might apply to your cloud? A short conversation is the simplest way to find out. We'll listen first, and only suggest something if it's likely to help. --- # Self-Funding Cloud Operations for Startups | skie.io URL: https://skie.io/aws-startups/ Description: Continuous optimization typically cuts AWS and Azure costs 25-50%, and the savings fund automation-first managed cloud operations. No new budget needed. ## Improve Cloud Efficiency. Self-Fund Continuous Cloud Operations. Continuous cloud optimization that generates measurable savings to help fund automation-first managed cloud operations for startups and growth-stage companies. Available across AWS and Azure, with enterprise multi-cloud support. skie typically delivers 25-50% cloud cost optimization through its continuous optimization platform, so organizations can self-fund modern cloud operations. $1.8M Annual savings · Liaison 40% Cost reduction · Liaison 2 weeks Initial savings plan ### No New Budget Required. Offset or fully fund managed services without adding headcount or new budget. Improve cost efficiency, governance, and operational maturity as your cloud environment grows. Available for qualifying AWS and Azure environments. Quick fit criteria #### Company size Organizations running production cloud workloads. IT not core business Lean teams #### Typical cloud spend Many organizations begin to realize meaningful self-funding opportunities around $10K/month in cloud spend, though lower-spend environments with significant optimization potential may also qualify. Bill shock Budget pressure Board visibility Cloud growth What you get fast: In ~2 weeks: Prioritized savings plan + Governance baseline + Operating model kickoff Guardian + Continuous Sage Optimization AWS Well-Architected SOC 2 Type II ### Why organizations choose skie.io. FinOps platforms optimize but don’t operate. Traditional MSPs operate but don’t continuously optimize. skie.io does both, continuously. Capability FinOps platforms nOps, CloudHealth, Spot Traditional MSPs Infosys, Rackspace skie.io Always-optimized partner Continuous optimization insight ✓ ✗ ✓ Automation-first operations ✗ Sometimes ✓ Cost governance is core (not optional) ✓ ✗ ✓ Managed cloud operations ✗ ✓ AWS Well-Architected + security controls ✗ Varies ✓ Typical optimization opportunity 15-20% 5-10% 25-50% ### Choose your starting point. Many organizations choose Guardian because it combines continuous Sage optimization with managed cloud operations. Organizations that prefer a phased approach can begin with Sage and add Guardian later. ★ Recommended Guardian (includes Sage) Managed cloud operations with continuous Sage optimization built in. - Automation-first managed cloud operations - Continuous Sage optimization included - Governance & security aligned to best practices - 25-50% optimization opportunities can significantly offset, or even fully fund, managed cloud operations. Sage Start with optimization. Add Guardian whenever you’re ready. - Full cost visibility across your cloud environment - Intelligent optimization recommendations - Automated savings execution - Pay only on achieved savings - Upgrade to Guardian for managed cloud operations at any time ### Customer results & proven expertise. $1.8M #### Liaison Education Annual savings with a 40% cost reduction. View case studies → $3M+ #### Navisite Annualized savings in seven months. View case studies → AWS Partner #### AWS Well-Architected Certified AWS partner with a proven methodology. View qualifications → Marketplace #### AWS Marketplace Available on AWS Marketplace for seamless procurement. View on Marketplace → ### Ready to see how much your cloud could fund itself? Get a free assessment and see your savings potential in 2 weeks. --- # Airth: Self-Funded Cloud Operations on AWS | skie.io URL: https://skie.io/case-studies/airth/ Description: How Airth.io lowered AWS unit costs with Sage and, from month two, covered its Guardian managed-services fees with verified recurring savings. ## Airth turned cloud efficiency into self-funded operations and budget for innovation. Targeted improvements across EKS, EC2 and RDS reduced Airth's AWS unit costs and lowered overall cloud spend, even as consumption continued to grow. After the initial ramp, verified recurring savings fully offset the cost of ongoing managed services. Double-digit Lower AWS spend First three months, despite growth ~35% Lower EC2 unit cost Baseline vs. month three 100%+ Managed-services coverage Verified savings vs. fees, after ramp Month 2 Full coverage reached Sustained in month three Airth.io Tucson, Arizona · Mining technology Airth.io's AI-powered platform connects planning, operations, mineral processing and finance so mine operators can find the value in their data. Its vendor- and commodity-agnostic SaaS platform delivers near-real-time data streaming and advanced analytics to a global customer base. Those data-intensive workloads run on AWS across EKS, EC2 and RDS: a compute-heavy environment where unit-cost efficiency flows straight to the bottom line. Results shown were measured during the first three months of the engagement. ### A growing AWS environment with significant room to improve unit economics. #### Growth ahead of visibility As workloads multiplied, engineering and finance wanted a single view of cost drivers across compute and Kubernetes. #### Headroom sized for peaks Node groups and pod requests provisioned for peak demand left EKS spend on the table between peaks. #### Scaling by hand Capacity could not yet follow demand automatically as workloads rose and fell. #### Operations ready to be codified Infrastructure changes were ready to move into automated, traceable workflows as the platform matured. Beyond cost. Alongside efficiency, Sage delivered an architect-led AWS Well-Architected Review covering security posture, IAM, logging and encryption, governance and tagging, reliability and operational guardrails, with prioritized remediation tracked from identified opportunity to customer-approved action to verified result. ### Improve cloud efficiency. Use the savings to fund operations. Free capacity for innovation. 1 #### Baseline Fix unit cost at engagement start so savings remain measurable as usage changes. 2 #### Improve Execute EKS, rightsizing, infrastructure-as-code and FinOps improvements. 3 #### Verify Compare actual blended unit cost with the baseline every month. 4 #### Reinvest Apply the lower operating base to fund operations and future growth initiatives. How the savings are measured. Effective savings are the reduction in AWS cost at Airth's actual workload volume, measured against the unit-cost baseline established at engagement start and stated before skie.io's optimization fees. After ramp, those savings fully covered Airth's ongoing managed-services fees. ### Lower spend, self-funded operations, capacity preserved. AWS consumption rose over the first three months while overall spend fell by double digits. Measuring unit cost isolates the savings created by skie.io from Airth's growth. #### Financial impact - Double-digit reduction in AWS spend despite consumption growth - Approximately 35% lower EC2 unit cost against the engagement baseline - Roughly 50% lower infrastructure cost across the specific EKS, EC2 and RDS resources addressed #### Operational impact - Managed-services fees fully offset by verified savings after ramp - Automated scaling, rightsized infrastructure and continuous optimization under Guardian - Karpenter, GitOps, Terraform and FinOps governance remain as residual value #### Business impact - A lower operating base and a stronger AWS foundation - Budget and operating capacity preserved for future innovation - Financial and operational room to pursue automation, AI and growth initiatives Double-digit Lower AWS spend First three months, despite growth ~35% Lower EC2 unit cost Baseline vs. month three 100%+ Managed-services coverage Verified savings vs. fees Month 2 Full coverage reached Sustained in month three ### Coverage reached full offset in month two and continued in month three. #### Month two: fully covered Verified effective savings reached parity with Guardian fees for the period after the first optimization wave landed. #### Month three: 100% covered Effective savings remained in line with Guardian fees for the period, demonstrating sustained coverage through new actions. "The impact of skie.io's recommendations was immediate. We saw a significant difference in efficiency and cost, and now have a much more scalable and optimized AWS environment." F. Erman Koc · CEO/CIO, Airth.io ### Sage finds it. Guardian keeps it. Muse is what the capacity is for. Improve · Sage Continuously identifies, quantifies and prioritizes cloud-efficiency and Well-Architected improvements, then verifies the realized results. Operate · Guardian Executes approved improvements, automates ongoing cloud operations and runs the AWS environment, turning Sage recommendations into sustained results. Innovate · Muse AI-driven automation agents for the operational work that follows. Not part of this engagement; the capacity it would need is what Airth now has. ### Self-funded cloud operations create capacity for innovation. By using recurring cloud savings to offset the cost of ongoing operations, Airth established a lower operating base and a stronger AWS foundation. Airth helps miners extract more value from every ton; skie.io helped Airth do the same with every dollar of cloud spend. That is the bridge from improving and operating the cloud to funding what comes next. Delivered with Sage for AWS · Guardian ### How much of your operations could your savings cover? A short, zero-risk assessment establishes your unit-cost baseline and shows where verified savings can offset operations and free budget for automation, AI and growth. --- # Supporting Cognitus Growth: A Case Study | skie.io URL: https://skie.io/case-studies/cognitus/ Description: How Cognitus saved $400K a year on cloud costs with skie while improving security and scalability as the business grew. ## Optimized by Sage. Run by Guardian. Grown fourfold. How Cognitus cut AWS costs by 42% with continuous Sage optimization, asked skie.io to run the environment under Guardian, and then grew its cloud about fourfold with AI and data workloads, without adding cloud-operations headcount. 42% Lower AWS costs In the first six months $400K+ Annual savings Continuous Sage optimization ~4× Cloud growth since AI and data workloads, under Guardian 0 Cloud-ops hires added Cognitus SAP consultancy · AWS Cognitus is an SAP consultancy that built its reputation on agile innovation and rapid growth. As it evolved from a fast-moving startup into a full enterprise IT organization, managing and scaling its cloud infrastructure brought new challenges. Its AWS footprint grew as the firm built and ran new applications for its clients and its own delivery platform. Cloud cost and governance practices had to keep pace with that growth without slowing the teams building on it. ### Rapid growth was creating a new cloud management challenge. Cognitus's AWS environment had expanded quickly as new applications were developed and deployed. Scale brought greater spend and complexity, together with the security, compliance, governance and accountability requirements of a large services organization. #### Control growing cloud spend Identify waste, improve commitment coverage, and ensure infrastructure and licensing costs reflected actual usage. #### Establish sustainable governance One-time savings would not be enough. Teams needed visibility, alerts and operating practices to manage cloud investment as the environment evolved. #### Preserve speed and scalability Governance could not become an obstacle to innovation. The new model needed to provide control while continuing to support growth. ### Optimization across the full cloud cost stack. Rather than treat each cost category separately, Sage considered how infrastructure, commitments, licensing, storage and operating practices affected one another, one coordinated program rather than a list of services. 1 #### Rightsizing and resource optimization Workload performance and utilization analyzed to identify overprovisioned services and align configurations with actual demand. 2 #### Savings Plans and Reserved Instances Usage patterns evaluated to determine which workloads suited commitment-based discounts while preserving flexibility for growth. 3 #### License and contract optimization Licensing arrangements reviewed for opportunities, including bring-your-own-license models, to align cost with usage. 4 #### Storage and data management Storage I/O, bandwidth, volume selection and backup lifecycles assessed to reduce cost without compromising protection or compliance. 5 #### Enterprise cost governance Real-time dashboards and automated alerts for visibility into spending patterns, so teams detect changes, manage budgets and act proactively. ### Lower spend. Stronger control. More capacity for growth. During the first 12 months, Cognitus cut cloud spend by over $400K a year, with AWS costs down 42% in six months, while establishing a scalable cost-governance framework. #### Financial impact - Reduced AWS costs by 42% in six months, over $400K a year - Better alignment between infrastructure cost and actual resource usage - Greater discipline around commitments, licensing, storage and backups #### Operational impact - Real-time cost visibility and automated alerts - Sustainable cloud cost-management practices - Teams identify and respond to cost changes proactively - Stronger operational oversight as the environment scales #### Business impact - Capacity to redirect resources toward innovation and strategic growth - Established enterprise cost-governance practices across a fast-growing application portfolio - Improved governance without slowing the teams building new applications 42% Lower AWS costs In the first six months $400K+ Annual savings Continuous Sage optimization ~4× Cloud growth since AI and data workloads, under Guardian 0 Cloud-ops hires added ### Optimized first. Then operated. Then it grew. Continuous Sage optimization brought AWS costs down 42% in the first six months. On the strength of that result, Cognitus asked skie.io to take on managed operations as well, and the environment it then added AI and data workloads to was already efficient and well-run. It has since grown roughly fourfold, without Cognitus adding cloud-operations headcount. #### Savings first Sage found and executed the rightsizing, commitment, licensing and storage improvements: 42% lower AWS costs in six months, over $400K a year. #### Then operations Cognitus extended the engagement to Guardian managed operations, so the gains were kept as the environment changed rather than eroding back. #### Then growth AI and data workloads landed on an optimized, operated baseline. The environment grew about 4×, with no new cloud-ops hires. ### Governance that can scale with the business. Cognitus now has a stronger foundation for balancing cloud efficiency with enterprise requirements and continued growth. As its environment evolves, the same model supports continued rightsizing, commitment management, cost accountability and architectural improvement, helping ensure that new cloud investment continues to create business value. Delivered with Sage for AWS · FinOps Capabilities ### Find your cloud savings opportunity. See how Sage can reduce unnecessary spend, strengthen control, and establish an optimization model designed to evolve with your business. --- # Cloud Case Studies: Migrations and Cost Savings | skie.io URL: https://skie.io/case-studies/ Description: Real results from skie customers: SAP moved to AWS in four weeks, $1.8M a year in AWS savings, and managed operations that keep the gains in place. ## Real cloud results. Measured. See how organizations make their cloud pay for its own operations, migrate and modernize critical workloads, strengthen day-to-day operations, and create capacity for what comes next with skie.io. Explore by All stories Self-funding cloud Migrate entry point Optimize Sage Operate Guardian Automate Muse SAP ### Complex cloud challenges. Clear, measured outcomes. Airth.io Mining technology · SaaS on AWS #### Cloud efficiency that pays for its own operations Targeted EKS, EC2 and RDS improvements lowered AWS spend while consumption grew. By month two, verified recurring savings fully covered Guardian managed-services fees. ~35% lower EC2 unit cost 100%+ managed-services coverage Month 2 full coverage reached Read the Airth story → San Fernando Leading Peruvian food company Part 1 #### VMware exit and SAP upgrade in one cutover, on a written savings commitment With the cloud decision open, AWS won on a written 20%+ commitment against prior infrastructure and managed-services spend. SAP and 40+ applications then moved off VMware in eight months, SAP upgraded in the same cutover. Operated by skie.io since, with a data platform and Muse GenAI agents added on the same estate. 20%+ committed in writing 40+ apps in 8 months Zero data loss Read the San Fernando story → ### What are you trying to improve? Start with the outcome your organization recognizes. Each one maps to a stage of the self-funding model, and to the customers who have been through it. → Migrate critical workloads Move SAP, VMware and data-center estates to AWS with continuity and control, and land them optimized. The entry point for many customers. JRC · San Fernando · Ceramics · Footwear · Networking 1 Improve efficiency Lower cloud spend and unit cost while protecting performance, reliability and business capacity. Cognitus · Liaison · Airth 2 Strengthen operations Run the cloud with more confidence: 24×7 monitoring, incident response, patching, backup, security and governance. JRC · San Fernando · Airth · Cognitus · Semiconductor 3 Create capacity Free room to scale and modernize by automating high-volume operational and business work. Netrio · San Fernando (Part 2) 4 Unlock innovation Put the capacity and budget your cloud frees up into AI, automation and growth. San Fernando (Part 2) · Netrio · Airth ### All customer stories 12 stories across migration, optimization, operations and automation. Airth.io Mining technology #### Self-funded operations and budget for innovation Double-digit lower AWS spend despite growth; Guardian fees fully offset by verified savings from month two. ~35% lower EC2 unit cost 100%+ fee coverage Month 2 full coverage Read case study → San Fernando Food production Part 1 #### VMware exit and SAP upgrade in one cutover, then continuous operations AWS chosen on a written 20%+ savings commitment; SAP and 40+ applications off VMware in eight months with an SAP upgrade in the same cutover, then continuous operations, a data platform and GenAI. 40+ applications Zero data loss 8 months Read case study → San Fernando Food production Part 2 #### From fragmented HR guidance to adoption in 60 days A guided HR assistant in Microsoft Teams, live in five weeks, on a governed foundation now extending to SAP-connected procurement. 60 days to full adoption 9 HR modules 5 weeks to production Read case study → JRC Multi-country mining group #### Accelerating M&A integration through cloud migration A mission-critical SAP S/4HANA environment moved from VMware to AWS in four weeks, with virtually zero downtime, and operated by skie.io since. 4 weeks 40% faster SAP transactions 99.995% availability Read case study → Netrio Managed service provider · 1,000+ clients #### 1,500+ engineering hours back every month, with Muse on every ticket Muse enriches, diagnoses and routes high-volume MSP tickets across ServiceNow, ConnectWise and Zoho, and surfaces service risk earlier. Measured December 2025. 1,500+ hours saved monthly 60,000+ tickets automated monthly 49+ automations live Read case study → Cognitus SAP consultancy #### Optimized by Sage, run by Guardian, grown fourfold AWS costs down 42% in six months, over $400K a year, then Guardian operations at the customer's request, and an optimized cloud that has since grown roughly fourfold with AI and data workloads. $400K+ annual savings 42% lower costs in 6 months ~4× growth since Read case study → Liaison Higher-education admissions platform #### $1.8M saved without sacrificing cloud performance AWS spend down 40% while maintaining 100% uptime through admissions deadlines and absorbing 10× traffic spikes. $1.8M annualized savings 40% cost reduction 100% uptime maintained Read case study → Packaging manufacturer Pharma and FMCG packaging · multi-plant #### ECC to S/4HANA in six months, with six hours of downtime Brownfield conversion with New GL migration and 100+ Fiori apps; 1,000+ custom objects carried forward and the database cut by 60%. 6 months to go-live 6 hrs technical downtime 60% smaller database Read case study → Ceramics manufacturer Tiles and sanitaryware · global #### S/4HANA 1909 live in two countries, on schedule through COVID-19 A highly customized, tightly integrated ECC landscape converted and re-integrated with CRM, MDM, DMS, PI and dealer portals, with a five-day development freeze. On time go-live 5 day dev freeze 2 countries live Read case study → Global footwear brand Footwear design, manufacture and retail #### A full SAP estate to HANA, with migration time cut in half ECC, BW, GRC and BusinessObjects moved from Oracle Database on premises to SAP HANA in SAP's managed private cloud; the ERP migration tuned from 160 hours to 77. 52% less migration time 60-70% faster key reports 4 systems moved Read case study → Optical networking manufacturer Telecom and networking hardware #### BW/4HANA in the cloud, with 50% faster key reports BW 7.4 on SQL Server converted remotely to BW/4HANA in SAP's managed private cloud, within a full-landscape move; history kept, redundant layers left behind. 50% faster key reports 33% lower BW maintenance 45% fewer redundant objects Read case study → Semiconductor company Network infrastructure · 11 countries #### 24×7 SAP and infrastructure operations at nearly 15% lower IT cost Senior engineers replaced an offshore 5×12 incumbent: SAP Basis and functional support, databases, infrastructure and network across 16 data centers, on budget. ~15% total IT savings 24×7 coverage, from 5×12 16 data centers standardized Read case study → ### Customers enter at different points. The model is the same. Some customers begin with a migration. Others begin with cost, operations or automation. Whatever the entry point, Sage savings fund Guardian operations, Guardian keeps the savings from drifting back, and the capacity that frees up funds Muse and what comes next. 1 Optimize Sage ↓ 2 Initial Savings ↓ 3 Maintained Guardian maintains efficiency ↓ 4 Capacity ↓ 5 Innovation Muse ↓ 6 Growth Optimize Cognitus Liaison Airth Initial savings Airth Maintained JRC Semiconductor San Fernando Airth Capacity Netrio Innovation San Fernando Netrio Growth San Fernando ### See how your cloud can fund what comes next. A short, zero-risk assessment is the simplest way to find out. We analyze your cloud, show where efficiency can fund operations and create capacity, and only suggest what's likely to help. --- # JRC AWS Migration: A Case Study | skie.io URL: https://skie.io/case-studies/jrc/ Description: JRC, a global mining group, moved SAP S/4HANA from VMware to AWS in four weeks after an acquisition, gaining 40% performance and 99.995% availability. ## Accelerating M&A integration through cloud migration. How JRC migrated a mission-critical SAP S/4HANA environment from VMware to AWS in four weeks, with virtually zero downtime and a 40% performance improvement, and how skie.io has operated it since. 4 weeks Complete SAP migration 40% Performance improvement Key SAP transactions 99.995% Availability Virtually zero Production downtime JRC Mining · Peru, Mexico, Canada JRC is a mining group with operations across Peru, Mexico and Canada. Following the acquisition of Compañía Minera Cuzcatlán (CMC), JRC needed to transition CMC's technology environment into infrastructure under its own control while creating a platform capable of supporting future expansion and modernization. ### An acquisition created an immediate technology deadline. The acquisition required JRC to separate CMC's VMware-based infrastructure and migrate its critical workloads under an aggressive transition timeline. Four priorities had to be met at once. #### Protect business-critical SAP operations Migrate SAP S/4HANA without disrupting ongoing business operations or compromising data integrity. #### Meet the acquisition timeline Complete the infrastructure separation quickly to support the broader acquisition and transition process. #### Meet enterprise governance and security requirements Support SAP workloads while aligning with JRC's corporate governance and security policies. #### Build beyond the immediate migration Rather than replicate the VMware environment, create a flexible, scalable cloud foundation for growth, modernization and faster provisioning. ### Moving fast without compromising control. skie.io combined the migration with the creation of a new AWS foundation, addressing the immediate separation requirement while establishing the architecture and operational controls needed for what came next. 1 #### Secure AWS foundation A multi-account environment with AWS Organizations and Control Tower: centralized identity, logging, governance, and preventive and detective guardrails. 2 #### Low-risk SAP migration AWS Application Migration Service replicated the VMware-hosted SAP servers to Amazon EC2 for a controlled production cutover with virtually zero downtime. 3 #### SAP-ready architecture S/4HANA and HANA deployed on SAP-certified AWS infrastructure sized for performance, storage, availability and backup. 4 #### Phased validation and cutover Development first, to validate connectivity, backups and Basis operations; production after validation; a new QA environment provisioned natively on AWS. 5 #### Operational readiness from day one Monitoring, security, backup, patching, inventory and compliance built into the environment as part of the migration, not added afterward. Technical foundation: built for SAP performance, security and resilience - Compute: Amazon EC2 r7i instances certified for SAP HANA workloads - SAP environment: SAP HANA 2.0 SPS07 and SAP S/4HANA 2023 on SUSE Linux Enterprise Server 15 SP5 - Storage: Amazon EBS gp3 and io2 volumes following SAP-certified storage patterns - Network and security: dedicated VPCs, segmented SAP application and database layers, least-privilege IAM, MFA, AWS KMS encryption - Backup and recovery: SAP HANA Backint integration with Amazon S3 and AWS Backup policies for the infrastructure layer - Monitoring and management: Amazon CloudWatch and AWS Systems Manager for monitoring, patching, inventory and compliance ### Migration completed. Operations improved. JRC met the acquisition timeline and moved its mission-critical SAP environment to AWS while improving performance, resilience, governance and day-to-day manageability. #### Business impact - Completed the SAP migration in four weeks - Met the infrastructure separation requirements created by the acquisition - Established a scalable AWS platform ready for future expansion and modernization - Enabled faster, on-demand provisioning of new cloud resources #### Operational impact - Virtually zero downtime during the production cutover - Reduced infrastructure maintenance burden through automation - Centralized account management, governance and security - Automated patching, inventory tracking and compliance monitoring #### Technology impact - Improved the performance of key SAP transactions by 40% - Achieved 99.995% availability - SAP-certified infrastructure optimized for HANA - Monitoring, security, backup and recovery integrated into the AWS environment 4 weeks Complete SAP migration 40% Performance improvement Key SAP transactions 99.995% Availability Virtually zero Production downtime ### From migration to continuous cloud operations. The engagement did not end at cutover. skie.io continues to operate JRC's SAP landscape and the supporting AWS environment under Guardian, moving the relationship beyond a one-time migration into a model focused on maintaining and continuously improving the environment. #### 24/7 monitoring and service desk integration Monitoring and alarms across the SAP and infrastructure layers around the clock. Alerts become service-desk tickets rather than notifications someone must notice. #### Incident management Defined severity levels and response times give JRC a clear process for issues affecting SAP availability or performance. #### Monthly patching Operating system patches go to non-production first, then to production after validation. #### Backup management SAP HANA backups via Backint to Amazon S3, with AWS Backup policies covering the infrastructure layer. #### Security and operational visibility AWS Security Hub and Amazon GuardDuty monitored continuously, with monthly reporting on inventory, utilization, SLA performance, security posture and backup status. #### Well-Architected Reviews Regular reviews against AWS best practices produce a prioritized remediation plan for continued improvement. ### Building on the AWS foundation. JRC and skie.io plan to continue expanding FinOps and optimization practices to improve cost transparency and identify ongoing opportunities. The secure, scalable, governed AWS foundation created through the migration is ready to support JRC's next stages of growth and modernization. Delivered with Cloud Migrations · Guardian ### Two clocks are running. Make one decision, not two. See how skie.io moves SAP and everything around it off VMware to AWS, lands it optimized, and operates it from day one. --- # Optimizing Cloud Costs for Liaison: A Case Study | skie.io URL: https://skie.io/case-studies/liaison/ Description: How skie helped Liaison, a higher education technology company, cut AWS costs by $1.8M a year without giving up performance or scalability. ## $1.8M saved without sacrificing cloud performance. How Liaison reduced AWS spend by 40% while maintaining 100% uptime and preserving the ability to handle 10× traffic spikes. $1.8M Annualized savings First 12 months 40% Cloud cost reduction 100% Uptime maintained Through admissions deadlines Liaison Higher-education admissions · 1,000+ programs worldwide Liaison International provides higher-education admissions management solutions used by more than 1,000 programs worldwide and processes millions of applications each year. The cyclical nature of admissions creates sharp demand peaks and immovable deadlines, making scalability, performance and availability business-critical. ### Reduce AWS spend without creating operational risk. Growth and complex infrastructure had increased cloud costs. Aggressive cost reduction could not come at the expense of the reliability and performance required during peak admissions periods. Three objectives had to be met together. #### Reduce infrastructure cost Identify and eliminate overprovisioning, inefficient commitments, unnecessary storage and licensing waste across the AWS environment. #### Protect critical availability Admissions deadlines meant downtime was not an acceptable trade-off for savings. Optimization decisions had to preserve service continuity. #### Maintain elastic performance The environment needed to keep handling significant traffic spikes while delivering a responsive experience when it mattered most. ### Treating cost, performance and commitments as one system. Rightsizing, infrastructure selection, commitments, licensing, storage and governance were evaluated together, so savings in one area would not create inefficiency or risk elsewhere. 1 #### Rightsizing and resource selection Utilization analyzed to identify overprovisioned instances and align configurations with actual workload requirements without reducing performance. 2 #### Savings Plans and Reserved Instances Workload patterns reviewed to build a balanced commitment portfolio, maximizing discounts while managing underutilization risk. 3 #### License optimization Licensed database services and deployment models assessed for opportunities, including bring-your-own-license configurations where appropriate. 4 #### Storage optimization Unused and infrequently accessed EBS volumes identified, volume types aligned with usage, and S3 lifecycle policies applied. 5 #### Cost governance and alerts Real-time dashboards and governance controls to identify and respond to potential overruns before they became embedded. ### Lower spend, with performance protected. Within the first 12 months, Liaison achieved a $1.8M annualized reduction in AWS spend while maintaining critical availability and preserving the elasticity peak admissions demand requires. #### Financial impact - AWS spend reduced by $1.8M annualized during the first 12 months - A 40% reduction in cloud costs - Capacity to reinvest savings in critical business initiatives #### Operational impact - 100% uptime maintained during critical admissions periods - Real-time visibility and control over cloud spend - Reduced risk of future cost overruns through governance and alerts #### Business impact - Preserved the ability to support traffic spikes of up to 10× during application deadlines - Protected the reliability and user experience of a mission-critical admissions platform - Better-informed decisions about cloud usage and investment $1.8M Annualized savings First 12 months 40% Cloud cost reduction 100% Uptime maintained Through admissions deadlines "skie.io has been instrumental in helping us optimize our cloud infrastructure. Their expertise allowed us to significantly reduce costs while actually improving our system's performance and reliability. This partnership has enabled us to better serve our mission of making higher education more accessible." Liaison International · Technology leadership ### Keeping savings from disappearing as the cloud changes. The optimization created more than a one-time reduction. Liaison gained the visibility and governance needed to monitor spending patterns, evaluate new usage and make better cloud investment decisions over time. #### Cost and architecture together Future savings do not come at the expense of availability, elasticity or performance. #### Governance and alerts Potential overruns are caught before they become embedded in the environment. #### Better investment decisions Visibility into spend supports decisions about new usage and platform investment. ### Turning cloud efficiency into business capacity. Liaison's results show that cloud optimization can create significant financial value while strengthening, not weakening, the environment. The savings and governance foundation give the organization more capacity to invest in the initiatives that support its customers, its platform and its mission. Delivered with Sage for AWS · FinOps Capabilities ### Find your cloud savings opportunity. See how Sage can reduce cloud spend while protecting the performance, reliability and scalability your business depends on. --- # Netrio: 1,500+ Engineering Hours Back with Muse | skie.io URL: https://skie.io/case-studies/netrio/ Description: How Netrio, a managed service provider, used skie.io Muse to enrich, diagnose and route high-volume tickets and get 1,500+ engineering hours back every month. ## Muse gives an MSP's engineers 1,500+ hours back every month. How Netrio used skie.io Muse to enrich, diagnose and route high-volume MSP tickets, and create real-time visibility into service risk. Results measured December 2025. 1,500+ Hours saved every month 60,000+ Tickets automated monthly 49+ Automations in production 30 min Saved per customer review Netrio Managed service provider · McKinney, Texas · Measured December 2025 Netrio is a North American managed service provider headquartered in McKinney, Texas, serving more than 1,000 small and mid-market clients with managed IT, cybersecurity, cloud, connectivity and voice services from offices in Texas, Minnesota, New York, Northern Ireland and India. Formed in 2024 by combining Netrio, SUCCESS Computer Consulting and PCA Technology Group, and expanded in 2025 with the acquisition of Agio, it handles thousands of tickets a month across ServiceNow, ConnectWise and Zoho. At MSP scale, the cost of every ticket is set less by the fix than by the preparation around it: finding the right context, running the first checks, and getting the work to the right engineer. ### Growth was outrunning the hours available for investigation. Netrio's ticket volume was growing faster than any team could staff for, and every ticket carried preparation work before an engineer could add real value. The opportunity was to give engineers that time back and turn operational data into faster, more consistent action. #### Three platforms, one picture ServiceNow, ConnectWise and Zoho each held part of the context; engineers assembled it by hand before they could act. #### Preparation before resolution Thousands of tickets a month needed context and first diagnostics before expert work could begin. #### Routing on experience Dispatch relied on the people who knew each account best, which made scaling and onboarding slower than Netrio wanted. #### Signals worth seeing sooner Customer sentiment surfaced in reviews rather than in real time; Netrio wanted to spot service risk earlier. ### Muse prepares each ticket before an engineer begins. skie.io deployed Muse across Netrio's existing operational platforms rather than replacing them. Engineers retain decision authority; automation gathers the evidence, applies approved logic and preserves operational oversight. 1 #### Enrich Collects the operational context required to understand each ticket from across the service platforms. 2 #### Diagnose Runs automated checks and prepares findings before an engineer opens the ticket. 3 #### Route Prioritizes and dispatches work by context and service level rather than by who happens to know the account. 4 #### Measure Tracks time reclaimed, automation performance and customer service signals continuously, so the operating model improves with the data. ### Measured MSP automation at scale. By December 2025 Netrio had converted automation into measurable capacity, handling more volume without proportional headcount growth. #### Business impact - 1,500+ engineering hours reclaimed every month - 30 minutes saved per customer review through automated preparation - Continued growth without proportional headcount expansion - A path to protecting customer retention through earlier service insight #### Operational impact - 60,000+ tickets automated every month - 49+ automations live in production across ServiceNow, ConnectWise and Zoho - Consistent routing by context and SLA across teams and platforms - Faster onboarding, since dispatch no longer depends on institutional knowledge #### Service impact - Engineers begin with context and diagnostics already prepared - Real-time customer signals surface dissatisfaction before it becomes churn risk - Lower cost to serve: routine investigation is automated before engineering time is applied 1,500+ Hours saved every month 60,000+ Tickets automated monthly 49+ Automations in production 30 min Saved per customer review ### An operating model that keeps compounding. The automations are not a one-time deployment. Muse runs inside Netrio's day-to-day operations under skie.io's governance model: approved logic, monitored performance, and engineers in control of every decision that matters. #### Governed automation Approved logic and full operational oversight; engineers keep decision authority. #### Measured performance Time reclaimed, automation performance and customer signals tracked continuously. #### Expanding coverage New ticket types and platforms are added to the same foundation, and the measurement layer shows what each one returns. ### From ticket volume to AI-orchestrated MSP operations. Muse connects fragmented service data, automation and performance insight into an operating model that scales without proportional headcount growth. The next step is extending the same model from ticket handling to proactive service management, using the customer signals Muse already surfaces. Delivered with Muse ### Turn fragmented service data into faster resolution and scalable capacity. Muse puts AI agents to work on the high-volume operational processes that consume your teams' capacity, governed from day one. --- # San Fernando HR Assistant with Muse: A Case Study | skie.io URL: https://skie.io/case-studies/sanfernando-genai/ Description: How San Fernando launched a guided HR assistant in Microsoft Teams with skie.io Muse: live in five weeks, full adoption in 60 days. ## From fragmented HR guidance to adoption in 60 days. How San Fernando used skie.io Muse to launch guided HR self-service in Microsoft Teams, and establish a reusable foundation for SAP-connected operational support. Full adoption In 60 days 9 HR modules deployed 50+ Guided interactions 5 weeks Kickoff to production San Fernando Food production · Peru · Part 2 of 2 Read Part 1: from VMware to continuous cloud operations → Founded in 1948, San Fernando is one of Peru's leading food companies, with vertically integrated operations spanning feed production, breeding, farming, processing and distribution. Its workforce spans multiple work centers with different needs, which made consistent HR service a challenge of navigation as much as of information. ### Employee support had become difficult to navigate. The opportunity was not simply to answer questions. It was to create a consistent, accessible service layer for employees. #### Fragmented HR knowledge Guidance was distributed across files, guides and process materials. #### Complex employee journeys Employees needed help across benefits, statutory rights, medical leave, pay slips, performance and procedures. #### Inconsistent navigation Support needed clear menus, fallback handling and escalation paths that worked across work centers. #### Disconnected next steps Answers also had to direct employees to systems, forms and the right HR contacts. ### A guided HR assistant built around real employee journeys. skie.io built the assistant on Muse and delivered it where employees already work: Microsoft Teams. Human control stays where it matters: approved content, access profiles, testing, escalation and monitoring keep service delivery governed as the program expands. 1 #### Organize Approved source content structured into owned topics and intent groups, so every answer traces back to HR-approved material. 2 #### Understand Conversational context and guided navigation identify the employee need. A nine-option entry point leads into structured flows with quick replies, back navigation and clear escalation. 3 #### Guide Consistent answers, next steps and escalation paths across work centers. 4 #### Connect Responses link employees to TuRecibo, Viva EsSalud, AFP resources, forms and HR contacts instead of stopping at information retrieval. The same layer can extend into SAP-connected workflows. ### Adoption, speed to production, and repeatability. San Fernando has demonstrated adoption, speed to production and repeatability. The value path now extends from faster access and consistent service to measurable reductions in routine inquiries, resolution time and HR effort. #### Business impact - Full adoption across the workforce within 60 days of launch - Consistent HR service across employee groups and work centers - A governed foundation that can support new functions without starting over #### Operational impact - Nine HR modules deployed, covering the highest-friction employee journeys - 50+ guided interactions in production - Routine navigation and follow-up shifted from HR staff toward self-service #### Delivery impact - Five weeks from kickoff to production - Delivered in Microsoft Teams, with no new application for employees to learn - Answers that connect to systems and contacts as well as documents Full adoption In 60 days 9 HR modules deployed 50+ Guided interactions 5 weeks Kickoff to production ### One governed foundation, many services. The HR assistant runs on the same skie.io platform that operates San Fernando's AWS environment. Content approval, access profiles, testing and monitoring are managed as part of the ongoing relationship, so new services are added to a governed foundation rather than built one-off. #### Approved content only Every answer traces to HR-approved source material, organized into owned topics. #### Access profiles Responses respect who is asking, the basis for the access-based procurement agent that follows. #### Monitoring and escalation Guided flows with fallback handling and clear escalation to the right HR contact when the assistant should not answer. ### The next agent: SAP-connected procurement. The program applies the same conversational model to purchasing operations, with access-based responses covering purchase orders, requisitions, suppliers, quantities, dates, currencies and release status: two access profiles (buyer and consulting views) and eleven defined behaviors designed around real purchasing questions. From guided answers toward operational action: this is how one HR assistant becomes a reusable AI service layer across the enterprise. Delivered with Muse ### Build a governed agent foundation that can expand across the enterprise. Muse puts AI agents to work on the high-volume operational and business processes that consume your teams' capacity, governed from day one. --- # San Fernando: VMware Exit and SAP Upgrade on AWS | skie.io URL: https://skie.io/case-studies/sanfernando/ Description: How San Fernando, Peru's largest poultry producer, moved SAP and 40+ applications off VMware to AWS in eight months on a written savings commitment. ## VMware exit and SAP upgrade in one cutover, then continuous cloud operations. How San Fernando chose AWS on a written savings commitment, moved its SAP estate and 40+ applications off VMware in eight months with an SAP upgrade in the same cutover, and has kept adding to the same foundation: continuous operations, a cloud-native data platform, and GenAI agents. 40+ Applications migrated Zero Data loss 8 months End-to-end transformation 20%+ Savings committed in writing vs. prior infrastructure and managed services San Fernando Food production · Peru Read Part 2: the Muse HR assistant → Founded in 1948, San Fernando is one of Peru's leading food companies, with vertically integrated operations spanning feed production, breeding, farming, processing and distribution. Its technology environment supports business-critical operations across production, supply chain, finance, quality and distribution, making continuity, performance and data availability essential. ### A growing business that had outgrown its infrastructure. San Fernando's VMware estate, split between its own data center and a hosting provider, had become complex and costly to maintain, and the cloud decision was still open. The company needed more than an infrastructure move: it needed to reduce operational risk, keep SAP supported, pay less than it was paying, and create a foundation that could adapt with the business. #### An estate at its limits at peak Performance and scalability constraints as operations and data requirements grew, affecting critical applications during periods of higher demand. #### An SAP upgrade coming due The SAP platform was approaching end of support and needed to be upgraded without disrupting the business processes that depended on it. #### Data in separate systems Business intelligence workloads were separated from other data sources, limiting visibility and slowing cross-functional analysis. #### Rising security and resilience requirements Stronger security controls, monitoring, backup and governance were needed for regulatory and operational requirements. #### Seasonal scalability Demand fluctuations required an environment that could scale faster and more cost-effectively than fixed on-premises infrastructure. ### One transformation across infrastructure, SAP, data and operations. skie.io designed and executed a phased cloud transformation that addressed the immediate migration while establishing the capabilities San Fernando would need after the move. 1 #### Portfolio assessment and migration planning Assessment of VMware-hosted applications, infrastructure dependencies and workload criticality, producing a phased plan with the right migration approach for each workload, and an AWS landscape designed optimized from the start so that its run cost could be committed to in writing. 2 #### SAP upgrade and migration San Fernando's core ERP platform was upgraded to a supported level and migrated to AWS infrastructure optimized for SAP in the same cutover, with a resilient architecture for availability, performance and growth. SAP stayed on ECC, keeping the S/4HANA decision open inside the original budget. 3 #### Application and infrastructure migration Critical SAP and non-SAP applications migrated using AWS practices and tools designed to minimize downtime and maintain business continuity. 4 #### Modern data platform A cloud-native data platform built alongside the migration: a data lake on object storage, an auto-scaling warehouse and serverless processing, giving teams faster access to information across production, supply chain, finance and quality. It runs as net-new AWS consumption inside the budget the commitment freed. 5 #### Security and operational readiness Private network architecture, least-privilege access, MFA, centralized monitoring, threat detection, audit trails, automated patching and backup management. ### A modern foundation with business-wide impact. The transformation moved San Fernando beyond infrastructure replacement. It delivered the committed savings, created a secure, scalable platform that supports critical operations and faster analysis, and left room in the budget for what came next. #### Business impact - AWS chosen on a written 20%+ savings commitment against prior infrastructure and managed-services spend, delivered as a step-down over three years - Critical applications and infrastructure migrated in eight months - Business continuity maintained throughout a complex transformation - Expanded access to data across production, supply chain, finance and quality teams - A platform capable of supporting future modernization and growth #### Operational impact - Migration completed without data loss - 24/7 monitoring, automated alerts and structured incident management established - Security and compliance controls strengthened - Ongoing cloud cost reviews and optimization practices introduced #### Technology impact - SAP upgraded to a supported level in the same cutover; S/4HANA decision kept open - Enterprise-level availability for production workloads - Improved application response times and faster analytics - A scalable cloud-native data and analytics foundation 40+ Applications migrated Zero Data loss 8 months End-to-end transformation 20%+ Savings committed in writing vs. prior infrastructure and managed services ### From migration to continuous cloud operations. The migration was the beginning of a broader operating relationship. San Fernando's AWS environment is run day to day by skie.io under Guardian, with continuous Sage optimization built in: monitoring, incident response, patching, backup, cost and architecture optimization in one operating model, delivered inside the committed price. #### 24/7 operations and incident management Monitoring and alarms cover SAP and the supporting infrastructure around the clock. Alerts open service-desk tickets automatically; business-impacting incidents are followed by root cause analysis. #### Patch and backup management Automated OS patching coordinated around agreed maintenance windows; managed backup routines with periodic restore tests. #### Continuous optimization with Sage Sage keeps analyzing the environment for cost, architecture and governance improvements; San Fernando approves what to act on and Guardian executes it, so the baseline does not drift as the estate grows. #### Architecture and risk improvement AWS Well-Architected Reviews generate a prioritized remediation plan, so improvements are scheduled by business and technical risk. #### Disaster recovery skie.io continues to work with San Fernando on the disaster recovery design and implementation plan for the AWS environment. #### The self-funding model Operations were delivered inside the committed price: the savings against the previous estate fund the operations that preserve them, and continuous optimization keeps the baseline from drifting. The same model made Airth's cloud self-funding. ### A cloud environment designed to keep improving. San Fernando's sequence is the self-funding model end to end: migrate the whole estate, operate it from day one, add a data platform on the same foundation, then GenAI. The Muse HR assistant in Microsoft Teams went live in five weeks and reached full adoption in 60 days; an SAP-connected procurement agent is next. Each stage landed on the same AWS estate, inside the budget the first one freed. Read Part 2: from fragmented HR guidance to adoption in 60 days → Delivered with Cloud Migrations · Guardian ### Two clocks are running. Make one decision, not two. See how skie.io moves SAP and everything around it off VMware to AWS, lands it optimized, and operates it from day one. --- # ECC to S/4HANA 1909 in Two Countries: A Case Study | skie.io URL: https://skie.io/case-studies/sap-ceramics/ Description: How one of the world's largest ceramics manufacturers moved a highly customized SAP ECC landscape to S/4HANA 1909 on schedule through COVID-19. ## S/4HANA 1909 live in two countries, on schedule through COVID-19. How one of the world's largest ceramics manufacturers moved a highly customized, tightly integrated SAP ECC landscape to S/4HANA 1909 with a five-day development freeze and a go-live date that did not move. On time Go-live despite COVID-19 5 days Development freeze 2 Countries live UAE and KSA 3 Systems on Azure DEV, QA, PRD Ceramics manufacturer Tiles, sanitaryware, bath and kitchen · Middle East and South Asia · Delivered by the skie.io SAP migration team A leading manufacturer of ceramic and porcelain wall and floor tiles and sanitaryware, running a network of plants across the Middle East and South Asia and selling globally. Its SAP ECC core was highly customized and tightly integrated: SAP CRM 7.x, MDM, DMS, Enterprise Portal and PI, plus procure-to-pay and a B2B dealer portal, with MDG governance and workflow spanning the landscape. ### A fixed go-live date and a landscape with no slack in it. The company needed its UAE and KSA operations on S/4HANA with a development freeze limited to five days, and the go-live was fixed before the pandemic arrived. The integration surface was the challenge: every connected system had to come across with it. #### Deep customization A large volume of Z-objects, Web Dynpro applications and client-specific enhancements adapted for the business over years. #### Tightly integrated CRM, MDM, DMS, Enterprise Portal, PI, procure-to-pay and a B2B dealer portal all depended on the core. #### Governance across two landscapes MDG and workflow spanned both the S/4HANA target and the ECC systems that would remain for other countries. #### A short freeze Five days of development freeze meant the team had to be certain before it started. ### Conversion led from the finance core outward. The team led the conversion and redesigned the finance and business processes that S/4HANA changes, with the target landscape on Microsoft Azure across development, quality and production. 1 #### Finance redesign Material Ledger migration, account-based CO-PA activated across the CO-PA and FI value flow, and new Asset Accounting. 2 #### Master data Overlapping customer, vendor and employee number ranges resolved for Business Partner; the accruals migration tool redesigned to client expectations. 3 #### Controls Credit Management deployed and authorizations redesigned for the new structures. 4 #### Custom code Z-objects, Web Dynpro apps and enhancements adapted to the S/4HANA 1909 structures. 5 #### Integration and cutover CRM, MDM, DMS, P2P, B2B and ancillary systems re-integrated; MDG and workflow stabilized across S/4HANA and ECC; ESS/MSS moved to Fiori. ### Delivered under pressure, with a modern finance core. The go-live date held with a hybrid team working onsite, offsite and from home as the pandemic set in. #### Business impact - Go-live on the committed date for the UAE and KSA operations - Material Ledger, account-based CO-PA and new Asset Accounting live - A foundation to consolidate every country into one harmonized S/4HANA instance #### Operational impact - Five-day development freeze held - All connected systems re-integrated with S/4HANA 1909 - MDG and workflow stable across the two landscapes after migration #### Technology impact - Full S/4HANA landscape running on Microsoft Azure - Employee and manager self-service on SAP Fiori - Credit Management and redesigned authorizations in place On time Go-live despite COVID-19 5 days Development freeze 2 Countries live UAE and KSA 3 Systems on Azure DEV, QA, PRD ### A heavily integrated conversion with the clock running. Most SAP estates are not one system; they are a cluster. This program shows the cluster moving together, with the integration map and the finance redesign done up front so the cutover window stays short even when the landscape is complex. #### The cluster moves as one CRM, MDM, DMS, portals and P2P re-integrated as part of the conversion, not after it. #### Finance changes planned, not discovered The S/4HANA finance model was redesigned deliberately rather than found in testing. #### Remote delivery that holds A hybrid onsite, offsite and offshore team kept the date through the pandemic. ### One instance for the enterprise. The next step is migrating the remaining two country instances to S/4HANA 1909 and consolidating every SAP instance into a single system with uniform processes across the enterprise. Delivered with Cloud Migrations ### Two clocks are running. Make one decision, not two. See the four S/4HANA paths, the 30-day assessment, and how skie.io moves SAP and everything around it, lands it optimized, and operates it from day one. --- # SAP Estate from Oracle to HANA: A Case Study | skie.io URL: https://skie.io/case-studies/sap-footwear/ Description: How a global footwear brand moved ECC, BW, GRC and BusinessObjects from Oracle to SAP HANA, cutting ERP migration time from 160 hours to 77. ## A full SAP estate to HANA, with migration time cut in half. How a global footwear brand moved ECC, BW, GRC and BusinessObjects from Oracle Database on premises to SAP HANA in SAP's managed private cloud, bringing the critical ERP migration down from 160 hours to 77. 160 to 77 hrs Total migration time 52% Less migration time 60-70% Faster key reports vs. the old BW 4 Systems moved ECC, BW, GRC, BO Global footwear brand Footwear design, manufacture and retail · US headquartered · Delivered by the skie.io SAP migration team A US-headquartered global footwear company that designs, manufactures and distributes shoes sold worldwide, running SAP ECC (AFS), BW, BusinessObjects, GRC and Portal. The SAP landscape sat on Oracle Database in a third-party data center, and end-to-end application performance was holding the business back as data volumes grew. ### Performance to gain, and a tight downtime window to do it in. The company wanted HANA performance across its SAP estate and a cloud foundation under it. The constraint was the ERP: a large database that had to move within a downtime window the business could accept, with encrypted transfer between the on-premises and cloud data centers. #### Reports slowed by data volume Large data volumes were slowing report generation across the platform. #### A complex BusinessObjects estate Many user instances and objects in user folders, all of which had to arrive intact. #### A large ERP database ECC had to move within a tight downtime window. #### Secure transfer Data had to move encrypted between the on-premises and cloud data centers. ### A migration tuned through repeated, measured test runs. The team applied its migration methodology and SAP Migration Factory tooling, running the migration repeatedly and measuring each pass until the production window fit. 1 #### Parallel migration Best-practice parallelization with table splitting and database tuning at source and target. 2 #### Infrastructure tuning Network monitoring and server resource tuning at both ends. 3 #### Security Encrypted data transfer between the on-premises and cloud data centers. 4 #### Cloud readiness Architecture review with sizing and scale-up recommendations for the target. 5 #### Code and BI Custom code remediated and optimized for HANA; BusinessObjects 4.2 built in the cloud with all content migrated and a structured test roadmap. ### Faster reporting, one BI platform, a shorter cutover. Days and weeks of optimizing the migration path brought the ERP migration from 160 hours to 77, coordinated across three teams in two data centers. #### Business impact - Big gains in report generation across the platform; key reports 60-70% faster than the old BW - A single unified BI platform - Lower cost of supporting the SAP environment #### Operational impact - Migration time halved: 160 hours to 77 - Delivered on time and to a high standard - Three teams in two data centers coordinated through one cutover plan #### Technology impact - ECC, BW, GRC and BusinessObjects on SAP HANA in the cloud - BW on Oracle converted to BW on HANA - A HANA cloud foundation in place for digital transformation 160 to 77 hrs Total migration time 52% Less migration time 60-70% Faster key reports vs. the old BW 4 Systems moved ECC, BW, GRC, BO "After days and weeks of optimizing the migration path, you successfully dropped the migration time from 160 hours to 77 hours. The optimization took the collaborative and combined effort of 3 teams in 2 data centers to get it done. We couldn't have done it without each of you." SAP Cloud Project Director · client organization ### The whole SAP estate, moved as one program. ECC, BW, GRC and BusinessObjects moved together, from Oracle Database on premises into SAP's managed private cloud, the path that today corresponds to RISE with SAP. The repeated, measured test runs are how a large database fits a small window. #### Rehearsed cutover Every pass measured and tuned; the production run was the one the team had already done. #### Oracle Database to HANA The database change was part of the move, not a separate project. #### BI arrives intact A structured test roadmap for the large BusinessObjects estate, refined through user feedback. ### A HANA foundation for what comes next. With the estate on HANA in the cloud and reporting running at the speed the business needed, the company has the platform in place for its digital transformation. Delivered with Cloud Migrations ### Two clocks are running. Make one decision, not two. See the four S/4HANA paths, the 30-day assessment, and how skie.io moves SAP and everything around it, lands it optimized, and operates it from day one. --- # BW 7.4 to BW/4HANA Remote Conversion: A Case Study | skie.io URL: https://skie.io/case-studies/sap-networking/ Description: How an optical networking manufacturer converted its BW 7.4 data warehouse to BW/4HANA in SAP's managed private cloud, with key reports running 50% faster. ## BW/4HANA in the cloud, with 50% faster key reports. How an optical networking manufacturer converted its BW 7.4 data warehouse on SQL Server to BW/4HANA in SAP's managed private cloud by remote conversion, as part of a full SAP landscape move. 50% Faster key report generation 33% Lower BW maintenance cost 30% Smaller data volumes 45% Fewer redundant objects Optical networking manufacturer Telecom and networking hardware · California headquartered · Delivered by the skie.io SAP migration team A California-headquartered, vertically integrated supplier of packet-optical networking solutions for Tier 1 carriers, internet content providers, cable operators, governments and enterprises. Its SAP BW 7.4 data warehouse ran on premises on SQL Server 2012 and was moving to SAP's managed private cloud as part of a wider landscape move: ECC to EHP7 on HANA, PI to PO 7.5, BusinessObjects 4.0 to 4.2 with LDAP single sign-on, MII 12.2 to 15.2, Fiori, and BPC 11 on BW/4HANA. ### Keep the history, change the platform, leave the clutter behind. The company had three requirements that ruled out the easy conversion options: retain all historical data, change database platform, and drop years of unused objects and redundant warehouse layers on the way. #### Keep every year of history All historical data had to come across, ruling out a shell conversion. #### Change the platform Moving off SQL Server ruled out an in-place conversion. #### Leave the clutter behind Old, unused objects and redundant warehouse layers were to be dropped, not migrated. #### Run both in parallel On-premises and cloud systems had to keep loading in parallel after go-live. ### A remote conversion: fresh build, selected objects and data moved across. The team used SAP's remote conversion method, building new BW/4HANA systems in the cloud across DEV, QA and PRD and transferring the selected objects and data from the on-premises system. 1 #### Prepare SAP Readiness Check, BW4 Note Analyzer, custom code checks, PSA clean-up and request compression. 2 #### Build New BW/4HANA systems provisioned with the DMIS add-on. 3 #### Convert Data flows collected and transferred through the Conversion Cockpit; extraction moved from SAPI to ODP. 4 #### Go-live Non-transportable objects adjusted, final validation, cutover. Hurdles overcome - Metadata transfer errors from transports and ABAP routines in transformations - Delta queues flushed before locking source data changes - InfoSets rebuilt by hand as CompositeProviders; non-cumulative key figures and modified Business Content handled - Scope refined through several sandbox iterations ### Leaner warehouse, faster insight, lower upkeep. Complex inventory and supply chain reporting runs 50% faster on a model with fewer layers, less redundancy and no aggregates or indexes to maintain. #### Business impact - Supply chain and inventory reporting 50% faster - Real-time reporting simplified through CDS views and HANA push-down - Quicker turnaround on business-critical insights #### Operational impact - BW maintenance cost down 33% - 30% of development effort saved - Fiori-based BW/4HANA Cockpit for modeling and monitoring #### Technology impact - Data volumes 30% smaller; 45% fewer redundant objects - Aggregates and indexes no longer needed - Platform in place for SAP Data Warehouse Cloud and BW bridge 50% Faster key report generation 33% Lower BW maintenance cost 30% Smaller data volumes 45% Fewer redundant objects "We started optimizing Data Warehouse layers, which in turn saved a lot of development effort and enabled us to provide a quicker turnaround time to generate business-critical key insights." Analytics team · client organization ### Conversion as an opportunity to simplify. The remote conversion is the BW equivalent of a selective transition: keep what is used, drop what is not, and arrive on the new platform lighter than you left. The same classification discipline applies to any ECC estate heading to S/4HANA. #### Usage decides scope Objects and layers that nobody used stayed behind; 45% fewer redundant objects. #### History preserved All historical data retained, which a shell conversion could not do. #### Part of a landscape move ECC, PI, BusinessObjects, MII and Fiori moved in the same program. ### Ready for what comes next in SAP analytics. With the warehouse lean and on BW/4HANA, the platform is in place for SAP Data Warehouse Cloud and the BW bridge, and for the analytics the plants and the supply chain ask for next. Delivered with Cloud Migrations ### Two clocks are running. Make one decision, not two. See the four S/4HANA paths, the 30-day assessment, and how skie.io moves SAP and everything around it, lands it optimized, and operates it from day one. --- # ECC to S/4HANA Brownfield in Six Months | skie.io URL: https://skie.io/case-studies/sap-packaging/ Description: How a packaging manufacturer converted a heavily customized SAP ECC to S/4HANA in six months, with six hours of downtime and a 60% smaller database. ## ECC to S/4HANA in six months, with six hours of downtime. How a multi-plant packaging manufacturer converted a heavily customized SAP ECC landscape to S/4HANA by brownfield conversion, carrying 1,000+ custom objects forward and cutting its database footprint by 60%. 6 months Start to go-live 6 hrs Production technical downtime 415 to 168 GB Database footprint After migration 1,000+ Custom objects carried forward Packaging manufacturer Pharma and FMCG packaging · Multi-plant · Delivered by the skie.io SAP migration team A specialist supplier of quality-assured, precision-folded cartons, inserts and outserts to leading pharmaceutical and FMCG brands, operating since the 1970s with in-house production and integrated quality control across multiple manufacturing plants. SAP had been the core business application since 2007, heavily tailored to packaging-industry needs: more than 1,000 custom ABAP objects, many output forms, and integrations to SAP BusinessObjects, DMS, barcode systems and a non-SAP automation engine. ### A mature ECC landscape ready for its next platform. Years of successful customization had made the ECC system central to the business, and also made the move to S/4HANA a careful piece of work. The company wanted the speed and simplification of S/4HANA without losing what made its SAP fit the business. #### Period-end close held back by batch dependencies Month-end depended on long batch runs; the business wanted a close it could run at any time. #### Reporting ready to be faster Error correction and reporting visibility lagged the pace of the plants. #### Customizations worth keeping 1,000+ custom objects and output forms carried real business logic and had to come across intact. #### Finance ready for New GL Classic GL was still in place, alongside GST changes and new S/4HANA notes to absorb. ### A brownfield conversion planned from the impact up. The team applied its conversion methodology, accelerated by its rapid-deployment cockpit, to assess technical, Basis and functional impact before anything moved. Scope: ECC conversion, landscape and business transformation, New GL migration, and 100+ standard Fiori apps across FI/CO, SD, MM, PP, QM, PM and DMS. 1 #### Impact assessment Custom code remediated to HANA standards via Code Inspector; Business Partner impact on vendor and customer master mapped; BRF+ output and direct-posting dependencies identified. 2 #### Finance first New GL facelift completed before conversion, with document splitting activated so compliance, including the GST changeover, was built in. 3 #### Sandbox Built from the latest production data; code migrated, converted and tested through several iterations. 4 #### QA Code freeze, downtime confirmation, migration, testing and bug fixing on the target landscape: S/4HANA application tier on Windows, HANA on Linux. 5 #### Production Validated cutover and go-live, with peripheral systems reintegrated within 24 hours. ### Faster close, simpler finance, smaller footprint. The conversion completed in six months with six hours of technical downtime and sixteen hours of functional downtime in production. #### Business impact - Month-end close sped up, with a soft close possible at any time - Reconciliation effort between Finance and Controlling eliminated - Return to standard: faster support and upgrades, less complexity to manage #### Operational impact - Six hours of production technical downtime; sixteen hours functional - Peripheral systems integrated within 24 hours of go-live - 100+ standard Fiori apps live across seven modules #### Technology impact - Database footprint reduced from 415 GB to 168 GB, about 60% smaller - 1,000+ custom objects remediated and carried forward - Document splitting and GST compliance built into the core 6 months Start to go-live 6 hrs Production technical downtime 415 to 168 GB Database footprint After migration 1,000+ Custom objects carried forward ### The path-B proof: customizations kept, change window contained. This is the one-step conversion path in practice. For an ECC estate where the custom code is worth keeping, brownfield gets the business onto S/4HANA with one cutover and one test cycle, and the impact assessment up front is what keeps the downtime to hours. #### Custom code travels Remediation to HANA standards means the Z-objects that carry business logic come across, rather than being rebuilt. #### Finance prepared ahead New GL and document splitting handled before conversion took the riskiest change out of the cutover window. #### Hardware cost down A 60% smaller database footprint lowers run cost on whatever infrastructure follows. ### A standard core, ready for the cloud. With the business on S/4HANA and back on standard, the landscape is positioned for the next move: SAP-certified infrastructure on AWS, operated from day one, with the custom periphery now cleanly separated from the core. Delivered with Cloud Migrations ### Two clocks are running. Make one decision, not two. See the four S/4HANA paths, the 30-day assessment, and how skie.io moves SAP and everything around it, lands it optimized, and operates it from day one. --- # 24x7 SAP Managed Services for Semiconductors | skie.io URL: https://skie.io/case-studies/sap-semiconductor/ Description: How a multinational semiconductor company moved from 5x12 offshore support to 24x7 SAP and infrastructure operations at nearly 15% lower total IT cost. ## 24×7 SAP and infrastructure operations, at nearly 15% lower total IT cost. How a multinational semiconductor company moved from 5×12 offshore support to 24×7 operations with senior engineers on SAP, databases, infrastructure and network, and met a 50% internal headcount target while project volume rose. ~15% Total IT savings 24×7 Support coverage Up from 5×12 16 / 11 Data centers / countries Standardized 500 Users on one platform Semiconductor company Semiconductors for network infrastructure · Multinational · Delivered by the skie.io SAP migration team A multinational designer of semiconductor solutions for wireline and wireless network infrastructure, selling to equipment manufacturers and recognized many times for technology excellence. Its IT estate spanned 16 data centers in 11 countries, with a highly customized, mission-critical SAP landscape at the center, supported by an incumbent provider billing senior rates for entry-level delivery. ### More from IT, for less, with a mission-critical SAP landscape in the middle. With tough economic conditions worldwide, leadership set out to get more value from IT for less: cut internal IT headcount by half, standardize a multi-location estate, stabilize SAP, and move to around-the-clock coverage. #### A 50% headcount target Leadership wanted the internal IT team halved without losing output. #### A multi-national estate to standardize 16 data centers across 11 countries, each with its own configuration. #### SAP to stabilize A highly customized, mission-critical landscape that needed to return to best-practice standards. #### Coverage and value 5×12 coverage from an incumbent whose rates did not match the skills delivered; 24×7 was the goal. ### Senior engineers, onshore for office hours, 24×7 overall, priced to the skills delivered. After a formal vetting process with reference checks, the CIO chose a provider that met the full wish list within budget and gave direct access to senior leadership. The model replaced 100% offshore, 5×12, entry-level delivery with onshore US office hours, 24×7 coverage and Level 2 engineers and above. 1 #### SAP Basis and functional support, stabilization and performance tuning for the customized landscape. 2 #### Databases Oracle Database and SQL Server administration and capacity planning. 3 #### Infrastructure Linux, AIX, Solaris and Windows; EMC and NetApp storage; NetBackup. 4 #### Network Scope later expanded to replace the WAN and LAN across all sites. 5 #### Governance Change management and proven management processes, with the team self-managing against agreed deliverables. ### Smaller team, more output, on budget. Every item on the wish list was delivered within budget, with total IT savings of nearly 15%. #### Business impact - Headcount goal met while project volume rose considerably - Every wish-list item delivered within budget - IT strategy, processes and procedures defined, with less management overhead #### Operational impact - 24×7 coverage, up from 5×12 - Mission-critical SAP back to best-practice standards - Stronger security, reliability and configuration management #### Technology impact - Common infrastructure across all business units and 16 data centers - 500 users on one standardized platform - WAN and LAN replaced across all sites ~15% Total IT savings 24×7 Support coverage Up from 5×12 16 / 11 Data centers / countries Standardized 500 Users on one platform "We are doing more with less staff because we have adopted the management processes. Without adding any additional staff, we have been able to standardize our environment and run more efficiently." CIO · client organization ### The operate half of the model, before the cloud. This is what Guardian does for an SAP estate after cutover, delivered here on premises: senior engineers, around-the-clock coverage, standardized infrastructure and governance that lets a smaller internal team do more. On AWS the same model runs automation-first, at a lower fee. #### Skills matched to price Level 2 engineers and above, priced to what is delivered. #### Standardization pays A common estate across business units cut management overhead and improved control. #### Operations that scale More project volume with fewer people, because the run was handled. ### From a standardized estate to an optimized cloud. A standardized, well-run estate is the starting point for a VMware exit: the dependency map already exists, the processes are defined, and the operating model carries straight into Guardian on AWS. Delivered with Guardian ### Two clocks are running. Make one decision, not two. See the four S/4HANA paths, the 30-day assessment, and how skie.io moves SAP and everything around it, lands it optimized, and operates it from day one. --- # Contact skie.io: Book a Free Cloud Assessment | skie.io URL: https://skie.io/contact-us/ Description: Talk to a senior cloud architect. We optimize cloud spend and use the savings to fund automation-first, SLA-backed cloud operations. ## Connect with skie.io Let’s uncover your immediate Cloud savings. Whether you want to just optimize your cloud or fully automate your infrastructure, our team is ready to help. We optimize Cloud spend and use the savings to fund enterprise-grade, automation-first cloud operations delivered by senior architects and backed by strict SLAs. Call Us At +1 (980) 349-7844 Email Us At info@skie.io. ### Please fill out the contact form below, and our team will reach out to you promptly. 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Your message has been sent successfully. Oops! Something went wrong. Please try again. --- # AWS & Azure Managed Services & Cost Optimization | skie.io URL: https://skie.io/ Description: One Intelligent Cloud Management platform that keeps AWS and Azure efficient, well run and ready for what comes next, funded by the savings it creates. ## Your cloud should become more valuable over time, not more expensive or more complex. Whether you're migrating to AWS or Azure, optimizing an existing environment, or modernizing cloud operations, skie.io provides one Intelligent Cloud Management platform that continuously improves your cloud: improving efficiency, strengthening operations, and creating capacity for innovation. AWS Advanced Tier Partner · AWS Well-Architected · SAP Competency · SOC 2 Type II ### Trusted by Leading Companies Liaison Education San Fernando Navisite Bolsa de Valores de Colombia Grupo Romero Pactum Netrio Cognitus Primax Ropaar Hallmark Neon Bank ### Migrate. Optimize. Operate. Automate. One connected operating model across the full lifecycle, from your first migration to mature, AI-driven cloud operations. Migrate - VMware exit & SAP modernization - Data-center migration - AWS & Azure migration Entry point Optimize - Cloud cost optimization - FinOps & governance - Architecture assessments Sage Operate - 24×7 managed cloud operations - Security & governance - Performance & reliability Guardian Automate - AI-powered operations - Muse agents - Continuous optimization Muse Migration is only the beginning. Most cloud providers help you reach the cloud. skie.io helps you continuously improve it. Cloud never stops changing. Organizations don't struggle because they're in the cloud. They struggle because it never holds still. Every application evolves Every deployment introduces change Every workload shifts Every business initiative reshapes your cloud Your cloud is in constant motion. If your cloud never stops changing, your cloud operations can't stop evolving either. #### Continuously optimize every part of your cloud, without waiting for the next project or support ticket. Cost Security Performance Resilience Governance Powered by Sage for continuous optimization and Guardian for intelligent cloud operations. ### Your cloud pays for its own operations. Sage finds the savings. Guardian runs your cloud and keeps the savings from drifting back. The capacity that frees up funds Muse automation and what comes next. One connected model, not three separate tools, so each part makes the next stronger. That is what makes the cloud self-funding. Optimization on its own erodes; operations on their own cost money. Together, the savings pay for the operations that preserve them, and the model compounds as your cloud grows. ### Savings that fund operations, and stay. Optimization establishes initial savings. Left alone, though, efficiency erodes as new workloads, AI, acquisitions, and architectural drift pull spend back up. Continuous Cloud Operations preserve those savings and compound them into lasting capacity for innovation. 1 Optimize Sage ↓ 2 Initial Savings ↓ 3 Maintained Guardian maintains efficiency ↓ 4 Capacity ↓ 5 Innovation Muse ↓ 6 Growth ### Optimization doesn't fail because recommendations are wrong. It fails because nobody owns execution. FinOps tool Recommendation Backlog Waiting on the team Never implemented Guardian Recommendation Automated execution Governed & monitored Continuously optimized Recommendations don't create value. Execution does. Guardian closes the realization gap, turning recommendations into measurable outcomes and keeping them that way as your cloud evolves. ### Three connected capabilities. Together, they compound. No matter where you are in your cloud journey, skie.io provides the platform to optimize, operate, and continuously improve your cloud environment. Sage Build a stronger cloud foundation. Optimizes spend, uncovers architectural strengths and weaknesses, improves security and governance, and creates the operational foundation for continuous cloud operations. → Guardian Continuously operate your cloud. Operates your cloud through automation-driven managed services, governance, monitoring, and senior engineering oversight. Guardian includes continuous Sage optimization as part of the managed operating model. → Muse Extend the model with AI. Muse extends the connected model with AI agents that automate operational and business processes using the knowledge Sage and Guardian create. Guardian establishes the operating model; Muse extends it into enterprise automation. Sage builds the foundation. Guardian continuously operates it. Muse automates the work on top. Together they create a cloud that becomes more efficient, more reliable, and easier to scale over time. ### Run your cloud, without the drag. Guardian is cloud operations powered by automation and AI: continuous Sage optimization plus automation-first operations, co-managed with your team or fully managed by ours. Self-Funding Cloud Operations #### Cloud operations that help fund themselves. Guardian includes continuous Sage optimization. As it operates your cloud, it continuously finds, preserves, and expands savings, helping offset the cost of Guardian while creating budget for innovation. See how skie.io pays for itself → Optimize with Sage Initial savings established Guardian preserves & expands savings Capacity for innovation & growth Every Guardian engagement includes - Continuous Sage optimization that doesn't stop after the assessment - 24/7 monitoring & automation-driven remediation, not ticket-chasing - Security & Well-Architected governance that runs continuously, not once a year - Enterprise-grade SLAs & senior engineering oversight See the full Guardian operating model → Built to improve, not only to maintain. Most managed services are designed to keep your cloud running. Guardian is designed to make it better while running it: efficiency, performance, security, governance, and operations, improving every day. Choose your operating model From co-managed (your team keeps control) to fully managed operations. ##### Guardian Flex Co-managed cloud operations You keep operational control. skie.io augments your team with continuous Sage optimization, monitoring, automation, security, patching, backup, and incident response. ##### Guardian Advanced Fully managed cloud operations skie.io takes operational ownership. We run infrastructure, landing zones, provisioning, governance, and ongoing operations, so your team focuses on applications and business priorities. ##### Guardian Premium Mission-critical cloud operations For business-critical environments. Adds enterprise SLAs, enhanced resilience, and disaster recovery on top of fully managed operations. ### Efficiency that creates room to grow. The efficiency gains are real and measurable, and they create the operational and financial capacity to scale, modernize, and innovate. Operational results 25-50% Typical efficiency improvement Continuous optimization across cloud environments. ~30 days Average onboarding From assessment to fully managed operations. 24×7 Continuously operated Monitored, governed, and continuously optimized. Customer results $3M Annual savings "We achieved over three million in annualized savings within just seven months, and that was merely the inception of our ongoing optimization initiatives." N Peter Salamanca COO, Navisite 25% Cost reduction "Their guidance led to a significant reduction in our AWS expenses within the first year. We're also considering further cost-saving strategies proposed by the skie.io team." bvc Diego Fernández Perdomo Legal Representative, Bolsa de Valores de Colombia 41% Cloud savings "We saved 41% on our cloud costs with skie.io. The process was painless and helped me divert critical resources to serve our customers and our core business." R Jitendra Singh CEO, ROPAAR ### A simple path from assessment to continuous cloud operations. Whether you're migrating, optimizing, or modernizing, every engagement begins the same way: a zero-risk assessment that maps your cloud, surfaces the highest-value opportunities, and points to the operating model that fits. 1 #### Zero-risk assessment Read-only access. We evaluate how your cloud is built, secured, and run, then deliver a roadmap in 14 days. No commitment. 2 #### Opportunity roadmap Architecture, security, governance, and optimization opportunities, with clear efficiency and savings projections. 3 #### Choose your operating model Whether that's Sage on its own, Guardian with Sage built in, or Guardian with Muse when you're ready. 4 #### Continuous improvement Optimization, automation, governance, and innovation that continue as your cloud evolves. ### Pick the path that fits your organization. Bring a complex cloud under control, or transform how you run cloud end to end. There's a path that fits: start with Sage on its own, or with Guardian, which includes continuous Sage optimization. For teams running their own cloud #### Optimize your cloud continuously For organizations that already operate their own cloud and want better visibility, governance, security, architecture, and cost efficiency. Many organizations start with Sage and later adopt Guardian to operationalize continuous optimization and cloud operations. Best fit if - Meaningful cloud investment (typically $15-20K+/month) - Internal cloud operations team - Looking for measurable efficiency and architectural improvements For teams transforming operations #### Transform your cloud operations Start with Guardian: continuous Sage optimization plus automation-driven cloud operations today, and add Muse when you're ready. For organizations that want continuous optimization, automation, governance, and fully managed cloud operations. Best fit if - Looking for ongoing operational support - Want automation-driven cloud operations - Planning for long-term cloud growth and AI adoption Many customers enter during a migration. VMware licensing changes and data-center exits are pushing organizations to modernize on AWS and Azure. skie.io establishes an optimized operating model from day one, then keeps delivering value long after the migration. Others begin with Sage or Guardian, based on where they are in their cloud journey. Facing a VMware renewal or the SAP ECC deadline? → Available for both AWS and Azure environments. ### An AWS Advanced Tier partner, qualified where it counts. Advanced Tier Partner Cloud Operations Competency SAP Services Competency View all AWS qualifications → Whether you're planning a migration, optimizing cloud costs, modernizing operations, or deploying AI-powered automation, skie.io provides one platform for continuous cloud operations. ### See how efficiency funds your next phase on the cloud. A short, zero-risk assessment is the simplest way to find out. We analyze your cloud, show you where Continuous Cloud Operations can create capacity for your next phase of growth, and only suggest what's likely to help. --- # Latest skie.io Platform Features | skie.io URL: https://skie.io/latest-features/ Description: Discover the latest features and updates to the skie.io platform for cloud cost optimization and management. ## Latest Platform Features ### Recent Updates #### July 2025 ##### Dynamic Table for AWS RDS Reserved Instances - Enhanced filtering, sorting, and export capabilities - Improved data analysis and cost optimization #### June 2025 ##### Tag Templates - Standardize resource tagging - Enforce consistent tagging policies - Reduce manual errors #### May 2025 ##### Dynamic Table for AWS Saving Plans - Improved data exploration - Easier identification of cost-saving opportunities #### March 2025 ##### Azure Database Charts Improvement - Enhanced visualizations - Interactive and customizable metrics #### February 2025 ##### Export Functionality for Dynamic Table - Multiple export formats - Streamlined data sharing #### January 2025 ##### Azure SQL DB Optimization - Optimize Managed SQL on Azure - vCore vs DTU, Bin Packing and much more ### 2024 Highlights #### Skie Assist Chat AI-powered assistance for cloud optimization queries #### RDS Recommendations Intelligent suggestions for database optimization #### API Integrations Seamless integration with third-party tools #### Elasticache Redis for ETL Enhanced data processing capabilities #### Admin Reports Comprehensive administrative insights #### Resource Groups Better organization and management of cloud resources ### Upcoming Features #### Unified Savings Dashboard (Q3 2025) Aggregate savings opportunities across services into a single dashboard #### Kubernetes Optimization (Q3 2025) Advanced container cost management with karpenter support #### Automated Optimization for RDS and EBS (Q3 2025) Apply optimization recommendations for RDS and EBS during patching process #### AWS OpenSearch and Elasticache Optimization (Q3 2025) Rightsize and optimize commitments --- # Partners: AWS, Microsoft Azure and SUSE | skie.io URL: https://skie.io/partners/ Description: skie.io is an AWS Advanced Tier Partner, a Microsoft Solutions Partner for Azure and a SUSE partner. See our partnerships, competencies and certifications. ## Strategic Technology Partners We partner with the world's leading cloud and technology providers to deliver comprehensive solutions that drive your business forward. ### AWS Differentiated Services and Software Partner As an AWS Advanced Tier Services Partner, skie demonstrates deep AWS expertise and proven customer success. We help organizations architect, build, migrate, and manage their workloads and applications on AWS. #### Advanced Tier Partner Recognized for technical proficiency and proven customer success #### Solution Provider Program Authorized to resell AWS services with integrated billing and support #### Well-Architected Partner Certified to review and optimize your AWS workloads against best practices #### Cloud Operations Competency Validated expertise in helping customers design, build, and manage cloud infrastructure #### Qualified Software Validated skie Sage Platform to be well architected #### SAP Services Competency Validated expertise in delivering SAP services on AWS #### What This Means for You ✓ ##### Experience AWS rigorous certification of experience and demonstrated success ✓ ##### Direct AWS Support Priority access to AWS technical resources and solution architects ✓ ##### Funding Programs Access to AWS funding for migrations, POCs, and optimization projects ✓ ##### Best Practices Implementation following AWS Well-Architected Framework principles ✓ ##### Cost Optimization Enterprise discount programs and reserved instance management ### Microsoft Solutions Partner for Azure As a Microsoft Azure CSP partner, skie provides complete Azure solutions with simplified billing, cost optimization services that can be purchased and billed through Marketplace. #### Azure designations Infrastructure (Azure) and Digital & App Innovation (Azure), awarded by Microsoft on customer success, skilling and performance. #### Azure CSP Benefits ##### Consolidated Billing Single invoice for all Azure services including Sage ##### 24/7 Support Direct support from skie experts, backed by Microsoft ##### Cost Management Azure cost optimization and reserved instance planning ### SUSE Partner for Enterprise Linux skie partners with SUSE to deliver enterprise-grade Linux solutions, including specialized support for SAP workloads on SUSE Linux Enterprise Server. #### SUSE Linux Enterprise - Enterprise Linux deployment and management - High availability and disaster recovery solutions - Security hardening and compliance - Performance optimization and tuning #### SUSE Linux for SAP Applications - SAP HANA certified deployments - SAP NetWeaver optimization - Automated SAP landscape management - 24/7 support for mission-critical SAP systems #### Enterprise Linux Benefits 🔒 Enhanced Security: Live kernel patching with zero downtime ⚡ High Performance: Optimized for cloud and SAP workloads 🛡️ Enterprise Support: 24/7 support with guaranteed SLAs 💰 Cost Effective: Reduced TCO compared to proprietary solutions ### Why Our Partnerships Matter #### Technical Excellence Our partnerships ensure access to the latest technologies, best practices, and technical resources from industry leaders. #### Cost Advantages Use our partner status for better pricing, funding programs, and cost optimization strategies. #### Support & Resources Direct access to partner support channels and technical resources for faster issue resolution. #### Innovation Access Early access to new features, beta programs, and emerging technologies from our partners. ### Leverage Our Partner Network Benefit from our strategic partnerships to accelerate your cloud journey. --- # Press Releases | skie.io URL: https://skie.io/press/ Description: Company announcements from skie.io, the Continuous Cloud Operations platform. ## Press Company announcements from skie.io. July 15, 2024 ### skie.io Welcomes Taylor Salazar as Vice President of Business Development Taylor Salazar, formerly Global AWS Solutions Provider ISV Strategy Lead at AWS, joins skie.io to drive customer acquisition and strategic partnerships. --- # Taylor Salazar Named VP of Business Development | skie.io URL: https://skie.io/press/skie-io-welcomes-taylor-salazar-as-vice-president-of-business-development/ Description: skie.io appoints Taylor Salazar, formerly of AWS, as Vice President of Business Development to drive customer acquisition and strategic partnerships. ## skie.io Welcomes Taylor Salazar as Vice President of Business Development Fort Lauderdale, FL – July 15, 2024 – skie.io, a leader in cloud cost optimization solutions, is excited to announce the appointment of Taylor Salazar as its new Vice President of Business Development. Bringing over 23 years of diverse business leadership and extensive knowledge of cloud technologies, Taylor will play a vital role in driving customer acquisition and expanding strategic partnerships. Before joining skie.io, Taylor Salazar spent 10 years at AWS as the Global AWS Solutions Provider ISV Strategy Lead. He also held various leadership roles, including Sr. Partner Development Manager supporting ISVs and SaaS companies to drive customer acquisition. During his tenure, he also worked as a Sr. Business Development Manager in the AWS Marketplace, helping businesses streamline their cloud operations and optimize costs. Taylor's proven track record of developing and executing successful business strategies complements skie.io's mission to deliver cutting-edge cloud optimization services. In his new role, Taylor will focus on identifying new business opportunities, fostering relationships with key stakeholders, and enhancing the overall customer experience. Sal Jamil, CEO of skie.io, shared his enthusiasm for the new addition: We are thrilled to have Taylor join our team. His extensive experience in cloud technologies and strategic business development, especially within the AWS ecosystem, is a perfect match for our company's growth aspirations. Taylor's vision and leadership will be instrumental in helping us achieve our goals and continue providing exceptional value to our clients. Taylor Salazar expressed his enthusiasm about the new position: I am excited to join skie.io and contribute to the company's mission of making cloud services more accessible and cost-effective. With the ever-increasing complexity of cloud infrastructure, businesses need intelligent solutions to manage costs and optimize resources. I look forward to leveraging my experience to help skie.io deliver unparalleled value to our customers. Taylor's appointment marks a significant step in skie.io's growth, as the company aims to expand its services and strengthen its presence in the cloud optimization industry. ### About skie.io skie.io is a technology company that makes cloud services more affordable and manageable. Through an advanced cloud optimization platform paired with senior Cloud DevOps experts, cloud-certified professionals and data scientists, skie.io provides comprehensive cloud cost optimization and management solutions. Its cloud management platform integrates seamlessly with existing cloud environments to offer real-time insights, cost reduction opportunities, and enhanced operational efficiency. For more information, please visit www.skie.io. #### Media Contact: Mauricio Jamil SVP Sales skie.io Email: mauricio.jamil@skie.io Phone: +1 980-349-7844 --- # Pay on Savings - Risk-Free Cloud Cost Optimization | skie.io URL: https://skie.io/pricing/ Description: Pay only for actual savings on your cloud costs. Start with a FREE assessment and benefit from our data-driven platform and expert guidance. ## Pay on savings: a risk-free investment in your success At skie.io, our clients' success is our top priority. We employ a unique payment model where our fees are directly tied to the actual savings we achieve on your cloud costs. ### Start an ongoing savings journey skie's sage combines a data-science driven platform with the knowledge of cloud-certified architects and FinOps specialists, leveraging diverse data points to provide holistic optimization recommendations, strategic guidance, and automated remediations. #### It all starts with a FREE assessment. ### Save first, Pay later Unlike other cloud management and cost optimization services that base their fees on your total cloud expenses, our pricing model is based on the savings actually achieved. In other words: pay nothing until you've achieved actual savings. Confidently optimize your cloud potential, knowing your success is our top priority! ### A tailored service powered by data-science This is how skie.io goes beyond cost control: we equip your company with tools to lower your cloud costs and drive forward-thinking solutions. Our comprehensive approach leverages a vast array of data points to uncover impactful solutions, considering inter-dependencies and commitments before making recommendations. Then our senior architects and FinOps specialists closely collaborate with your team to implement those and reach your unique goals. Payment skie.io Pay a percentage of your actual savings #### Competitor A Pay based on your total cloud bill #### Competitor B Recurrent flat fee FREE assessment skie.io White glove assessment #### Competitor A General Analysis #### Competitor B Recurrent flat fee Managed FinOps skie.io #### Competitor A #### Competitor B Analysis Model skie.io We consider decision interdependencies; they don't #### Competitor A Isolated decisions #### Competitor B Isolated decisions Implementation skie.io Guided by senior architects and FinOps Specialists #### Competitor A Do it yourself #### Competitor B Do it yourself ### Uncover potential savings and optimize your cloud infrastructure with confidence Learn your cloud savings potential with skie.io's complimentary White Glove Assessment. Powered by skie Sage, our assessment delves into your actual cloud spending data, providing a holistic view of your entire cloud footprint. With no privileged access to your servers required, it's hassle-free and secure. And, unlike other providers, it is not based on samples but analyzes 100% of your metadata, ensuring precision and personalized insights. ### Our cloud improvement process #### Data Ingestion Usage and cost telemetry is automatically gathered from customer cloud environments over time. #### skie Sage Advanced algorithms analyze past and forecasted data, commitments and licenses, to find cost savings opportunities. #### Tailored Execution Plan skie.io experts review the recommendations and create a tailored plan for implementation. #### skie Guardian Implementation commands are prepared and automatically executed via API. --- # Privacy Policy | skie.io URL: https://skie.io/privacy-policy/ Description: Learn how skie.io collects, uses, and protects your personal data across our website and cloud optimization platform, and the privacy rights available to you. ## Privacy Policy Effective Date: January 2025 | Last Updated: January 2025 At skie.io ("skie," "we," "us," or "our"), we take your privacy seriously. This Privacy Policy explains how we collect, use, disclose, and safeguard your information when you use our website, platform, and services. ### 1. Information We Collect #### Information You Provide to Us ##### Account Information - Name, email address, phone number - Company name and job title - Billing and payment information - Account credentials ##### Service Data - Cloud infrastructure configuration data - Usage patterns and metrics - Cost and performance data - Business process information for automation - Support tickets and communications ##### Communications - Emails, chat messages, and phone recordings (with consent) - Feedback and survey responses - Marketing preferences #### Information Collected Automatically ##### Technical Information - IP address and device information - Browser type and operating system - Access times and referring URLs - Pages viewed and features used ##### Cookies and Tracking Technologies - Session cookies for authentication - Analytics cookies to improve our services - Preference cookies to remember your settings - Marketing cookies (with your consent) #### Information from Third Parties - Cloud provider data (AWS and Azure) with your authorization - Business verification services - Marketing and analytics partners - Public sources and databases ### 2. How We Use Your Information We use your information to: #### Provide and Improve Our Services - Deliver cloud optimization recommendations - Execute managed services and automation - Process transactions and billing - Provide customer support - Improve our platform and develop new features #### Communicate with You - Send service-related notifications - Respond to inquiries and support requests - Provide updates about new features - Send marketing communications (with consent) #### Ensure Security and Compliance - Detect and prevent fraud - Monitor for security threats - Comply with legal obligations - Enforce our Terms of Use #### Business Analytics - Analyze usage patterns and trends - Measure service performance - Conduct market research - Generate aggregated insights ### 3. How We Share Your Information We do not sell, trade, or rent your personal information. We may share information in these circumstances: #### Service Providers We share data with vendors who help us provide our services: - Cloud infrastructure providers - Communication platforms - Security and monitoring services #### Business Transfers If skie.io is involved in a merger, acquisition, or sale of assets, your information may be transferred as part of that transaction. #### Legal Requirements We may disclose information if required by law or if we believe disclosure is necessary to: - Comply with legal obligations - Protect our rights and property - Prevent fraud or security issues - Protect the safety of any person #### With Your Consent We may share information with third parties when you explicitly consent to such sharing. ### 4. 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Contact Us If you have questions about this Privacy Policy or our privacy practices, please contact us: Data Protection Officer skie.io, LLC 110 East Broward Blvd, Suite 1700 Fort Lauderdale, FL 33301 USA Email: privacy@skie.io For EU residents: dpo@skie.io ### Additional Information for Specific Services #### Sage (Cloud Optimization) - We access cloud metadata with read-only permissions - Cost and usage data is analyzed to provide recommendations - Historical data is retained for trend analysis #### Muse (AI Automation) - We process business data to create automation workflows - AI models do not train on your data - Conversation logs are retained for quality and support #### Guardian (Managed Services) - We access infrastructure for management purposes - Security logs and metrics are continuously monitored - Backup data is retained according to your retention policy Your privacy is important to us. If you have any concerns, please don't hesitate to contact us. --- # Guardian: AWS Managed Services and 24/7 Support | skie.io URL: https://skie.io/products/guardian/ Description: Guardian runs your AWS environment 24x7 and keeps optimizing it, so for most customers the savings cover the fee. Plans, SLA guarantees and a free assessment. ## Managed cloud operations that pay for themselves. We run your cloud environment 24×7 and keep optimizing it as we go. For most customers, what we save covers what we charge. 100%+ of Guardian fees covered by verified savings at Airth.io 30 min Sev1 response, every tier · 15 min on Premium 99.9%+ availability, with credits applied automatically AWS Advanced Tier Partner · AWS Cloud Operations Competency · AWS Well-Architected · SOC 2 Type II ### Your cloud keeps changing. Operations have to keep up. Every deployment, workload and new initiative reshapes the environment. The two usual ways of running it both fall behind, for different reasons. Running it in-house Your best engineers end up on patching, alerts and tickets. • Nights and weekends are best-effort, not real 24×7 coverage • One or two people hold the knowledge: key-person risk • Optimization waits for the next project; spend drifts up between them • Patching and configuration drift between teams and accounts Traditional managed services Staffed by headcount, so the fee scales with your estate. • Reactive: the ticket queue is the operating model • Generic runbooks that don't learn your environment • Cost, operations and automation in separate tools and teams • Savings get recommended, then wait in a backlog Guardian takes operational ownership. We hold and run the environment. Automation does the routine work, senior engineers handle what needs judgment, and optimization runs continuously as part of operations. That is why the fee is lower than a staffed MSP, and why the savings can pay for it. ### Optimization is part of the job, not a separate project. One loop runs every day. Recommendations become results instead of backlog. 1 #### Identify Sage, our optimization engine, continuously analyzes the environment: right-sizing, commitments, storage, idle resources and Well-Architected gaps, each quantified. 2 #### Approve You see every opportunity with its value and risk and approve what moves. Nothing changes in your environment without your sign-off. 3 #### Execute Guardian engineers implement in maintenance windows: right-sizing, patching, backups, security fixes. The result shows up on your AWS invoice. 4 #### Codify Muse, our AI automation layer, turns each fix into a runbook so the next occurrence is handled automatically, and your environment gets easier to run over time. What that looks like at 3 AM An alert fires. Muse enriches the incident with context from your environment, applies the known remediation first and wakes an engineer only if it doesn't resolve. Routine incidents close on their own; your team and ours handle the ones that need judgment. ### Everything it takes to run your cloud well, in one service. #### Well-Architected operations 24×7 monitoring and incident response, automated OS and database patching, backup and disaster recovery, change management. Built on AWS Well-Architected practices. #### Continuous optimization Sage runs inside Guardian. Savings are identified, approved, executed and tracked, and on resold accounts they appear on the same AWS invoice as the fee. #### Security built in SSO with enforced MFA, GuardDuty and CrowdStrike Falcon threat detection, Inspector vulnerability scanning, AWS Shield, monthly patching. SOC 2 Type II; HIPAA- and PCI-ready configurations. #### Total transparency Shared dashboards, approval gates on every change, monthly reporting and SLA credits applied automatically. You always know what we did and why. Fully managed by Guardian - Compute and storage: EC2, EBS, EFS, FSx - Load balancing, VPC and networking - Backup, recovery and disaster recovery - Operating systems, patching and license management - Identity, access and security tooling Provisioned and configured on request - Containers and serverless - Managed databases - Advanced security services: WAF, GuardDuty, Shield - Analytics and 40+ other cloud services - Included consulting hours cover these, project support and vulnerability remediation ### Operated since cutover, and still improving. Every account below is referenceable. Ask us for the contact and we will arrange the call. Mining technology #### Airth.io An AI analytics platform for mine operators, running data-intensive workloads on EKS, EC2 and RDS. Sage found the savings; Guardian took over operations. By month two, verified savings covered the full Guardian fee, and they have stayed there. 100%+ fees covered by savings Month 2 full coverage reached Read the case study Food production #### A large vertically integrated food producer VMware exit and SAP upgrade in one cutover, on a written 20%+ commitment against the previous infrastructure and managed-services spend. Guardian has operated the estate since, and it has grown to include a data platform and GenAI agents. 20%+ committed in writing 40+ applications in 8 months Read the case study Mining #### A multi-country mining group SAP S/4HANA moved off VMware to AWS in four weeks with zero incidents. Guardian has operated the environment since cutover. 4 weeks to cutover 0 incidents Read the case study SAP services #### Cognitus Sage cut AWS costs 42% in six months. On that result the customer asked skie.io to add Guardian, and the optimized environment has since grown about fourfold with no added cloud-ops headcount. 42% lower AWS costs ~4× growth, 0 hires Read the case study See all managed operations stories → ### Three levels of coverage. One operating model. Choose the control, response and resilience your workloads need. Every tier includes continuous optimization, and the savings it finds go toward the fee. Flex Monitoring and response. You keep control. Includes - 24×7 monitoring and alerting - Sev1/2 incident response, 30-minute SLA - Security management - Automated OS and database patching - Backups, 24-hour RPO - Monthly operational review You handle: resource provisioning and access management. Most popular Advanced Full infrastructure management. Everything in Flex, plus - Complete infrastructure management - Landing zone and governance - Provisioning and changes - Identity and access management - Pre-production environments - Backup restoration, 4-hour RTO - Weekly change meeting You keep: read-only access and application deployment rights. Premium Mission-critical resilience. Everything in Advanced, plus - 15-minute Sev1 response, priority queue - 3-hour Sev1 resolution SLA - 20-minute RPO, 2-hour RTO with AWS Elastic DR - Regional disaster recovery - Executive quarterly reviews Best for: SAP and other systems the business cannot run without. Start on Flex and move up when you are ready: Flex to Advanced after an environment audit, Advanced to Premium immediately. The fee is set against the savings the assessment identifies, so you see both numbers side by side before you commit. #### Measurable response. Predictable recovery. SLA guarantee Flex Advanced Premium Sev1 response 30 min 15 min Sev1 resolution Not included 3 hours Backup RPO 24 hours 20 min Backup RTO Not included 4 hrs / TB Disaster recovery RPO Not included 24 hours 20 min Disaster recovery RTO Not included 48 hours 2 hours Availability 99.9% 99.99% Missed guarantees are credited automatically. No claim to file. ### Up and running in two weeks, then a steady rhythm. 1 #### Weeks 1-2: onboarding Environment assessment, security audit and remediation, monitoring set up, runbooks created with Muse from how your environment actually behaves, and introductions to your named team. 2 #### Ongoing cadence Weekly change control (one hour), monthly performance and savings review (one hour), quarterly executive session. Behind that, 24×7 monitoring and continuous optimization. 3 #### Your team A named Cloud Architect and Technical Account Manager, the 24×7 operations team, security specialists and FinOps experts. The people who onboard you stay accountable. ### See the numbers before you decide. A free assessment with read-only access to your cloud account. Nothing changes in your environment. We show where the savings are, what Guardian would cover and what it would cost against those savings. You decide. No commitment. The findings are yours whether or not you choose Guardian. - Quantified optimization opportunities Right-sizing, commitments, storage and idle resources, each with its monthly value - Operations baseline What you run today, how it is covered, and where the gaps are - Recommended tier and fee Which Guardian plan fits, what it costs, and how much of it the savings cover - Onboarding plan What happens in the first two weeks and what we need from your team ### Run your cloud with a team that keeps improving it. Start with the assessment, see what the savings cover, and let Guardian take the pager from there. --- # skie Muse - Enterprise AI for Labor Productivity | skie.io URL: https://skie.io/products/muse/ Description: Muse deploys enterprise-ready GenAI applications in weeks, using pre-built AI infrastructure and expert guidance to automate repetitive work. ## Turn Every Employee into a 10x Performer with AI Deploy enterprise-ready GenAI applications in weeks, not years. Muse combines pre-built AI infrastructure with expert guidance to automate repetitive work and amplify human intelligence across your organization. 70% faster incident resolution 60% reduction in manual tasks 2-4 weeks to first AI application ### Your Teams Are Drowning in Repetitive Work Every day, your highest-paid experts spend hours on tasks that could be automated: Engineers searching through past incidents and documentation Finance teams manually compiling reports from multiple sources Support staff answering the same questions repeatedly Operations copying data between systems Everyone context-switching between tools and losing productivity Meanwhile, your competitors are using AI to do more with less. ### Why Most Enterprise AI Projects Fail ❌ Starting from scratch with infrastructure ❌ Lacking AI expertise to identify use cases ❌ Security and compliance concerns ❌ No clear ROI or success metrics ❌ Integration nightmares with existing tools ### Introducing Muse Your Fast Track to Enterprise AI Success Muse is a complete AI automation platform that combines: #### Pre-built AI infrastructure ready for enterprise scale #### Multi-LLM orchestration using best model for each task #### Native integrations with Teams, Slack, and your tools #### Expert AI consultants to identify and build use cases #### Sprint-based delivery for rapid value realization ### The Muse Advantage #### Production-Ready Platform Skip months of infrastructure setup with our secure, scalable AI landing zone #### AI Expertise Included Our AI architects work alongside your team to identify and implement high-impact use cases #### Seamless Integration Pre-built connectors to your communication and business tools #### Measurable ROI Track productivity gains and cost savings from day one #### GenAI Security and Data Privacy Enterprise-grade security with SOC 2 compliance, data isolation, and role-based access controls to protect your sensitive information #### GenAI Cost Optimization Smart model routing and usage monitoring to minimize AI costs while maximizing performance and output quality ### Transform Every Department with AI #### IT Operations: 70% Faster Incident Resolution ##### The Challenge: Engineers spending hours researching similar issues ##### The Muse Solution: AI enriches every incident with: - Similar past incidents and resolutions - Relevant knowledge base articles - Current monitoring status and dependencies Plus: - Natural language queries across all documentation - Automated remediation for common issues "Our MTTR dropped from 4 hours to 1.2 hours. Engineers now focus on prevention instead of firefighting." - Global Technology Company #### Finance: Automated FP&A Reporting ##### The Challenge: Days spent compiling data and creating reports ##### The Muse Solution: AI generates draft reports with: - Multi-source data analysis - Variance explanations - Trend identification - Executive summaries Plus: - Natural language queries for ad-hoc analysis - Automated insight generation "What took 3 days now takes 3 hours, and the insights are deeper than ever." - Fortune 500 CFO #### Customer Service: Zero-Touch Resolution ##### The Challenge: Repetitive tickets consuming agent time ##### The Muse Solution: - RAG-powered knowledge retrieval - Natural language understanding - Automated action execution - Seamless handoff when needed "40% of tickets now resolve without human intervention, and customer satisfaction increased 25%." - Major Service Provider ### Customer results AI automation skie has delivered with Muse #### Netrio Managed service provider. Muse enriches, diagnoses and routes high-volume tickets, giving engineers 1,500+ hours back every month. Read the Netrio case study → #### San Fernando HR assistant A guided HR assistant in Microsoft Teams built with Muse: live in five weeks, full adoption in 60 days. Read the San Fernando HR assistant case study → ### From Idea to Impact in 4 Sprints Sprint 0 - Week 1 #### Assessment - Map your high-impact, repetitive processes - Identify quick wins and transformational opportunities - Build prioritized AI roadmap - Define success metrics Sprint 1 - Weeks 2-3 #### Foundation - Deploy Muse platform in your environment - Configure LLM orchestration - Build and test first AI application - Integrate with your tools (Teams/Slack) Sprint 2 - Weeks 4-5 #### Scale & Enhance - Launch to broader user group - Gather feedback and optimize - Add second use case - Implement advanced features (RAG, automation) Sprint 3 - Week 6+ #### Production - Full production deployment - Performance monitoring and optimization - Knowledge transfer to your team - Plan next AI initiatives ### Platform Architecture #### Secure AI Landing Zone - Private deployment in your cloud - SOC 2 Type II compliant - Data never leaves your environment - Role-based access control #### Multi-LLM Orchestration - Best model for each task - Cost optimization built-in - Fallback and redundancy - Latest models always available #### Enterprise Integrations - Microsoft Teams native app - Slack workspace integration - API gateway for custom apps - Webhook automation ### The Muse Platform #### Core Capabilities ##### Intelligent Orchestration - Route requests to optimal LLM - Manage token limits and costs - Handle rate limiting automatically - A/B test different models ##### RAG (Retrieval Augmented Generation) - Connect to your knowledge bases - Real-time data retrieval - Source attribution and compliance - Continuous learning from feedback ##### Automation Engine - Natural language to action - Multi-step workflow execution - Human-in-the-loop when needed - Audit trail for compliance ##### Analytics & Monitoring - Usage and cost tracking - ROI measurement dashboard - Performance metrics - User adoption analytics #### Pre-Built Connectors ##### Communication Platforms - Microsoft Teams - Slack - Email (Outlook/Gmail) ##### Knowledge Sources - SharePoint - Confluence - ServiceNow - Custom databases ##### Business Systems - Salesforce - SAP - Workday - Custom APIs ### Why Muse vs. Build Your Own The Smart Path to Enterprise AI Challenge DIY Approach Muse Solution Infrastructure Setup 6-12 months 1 week LLM Integration Complex custom code Pre-built orchestration Security & Compliance Your responsibility Enterprise-ready Use Case Identification Trial and error Expert guidance Tool Integration Months of development Native connectors Ongoing Optimization Dedicated team needed Managed service Total Time to Value 12-18 months 2-4 weeks ### Security & Compliance Enterprise-Grade AI Security #### Data Privacy - Deployed in your cloud environment - No data training on your information - Complete audit trails - Zero data retention by LLMs #### Compliance Built-In - SOC 2 Type II audited, report available on request - GDPR compliant architecture - AWS Well Architected - Role-based access control #### Governance Features - Prompt injection protection - Content filtering controls - Usage policies enforcement - Cost controls and limits --- # Sage for AWS: Cost Optimization & Managed FinOps | skie.io URL: https://skie.io/products/sage/aws/ Description: Sage right-sizes EC2, ECS, EKS, RDS and EBS across your AWS estate, plans commitments in the same pass, and applies changes in your patch window. ## Cost decisions that account for each other Sage is skie's cost optimization engine for AWS. It right-sizes your resources, from compute through databases and storage, plans which commitments to buy, and reconciles the two in a single pass, because changing one changes the answer to the other. Then it applies the changes for you, inside a maintenance window you have already approved. Efficiency that frees budget and engineering time for what you build next on AWS. Available through AWS Marketplace. AWS Advanced Tier Partner · Automated implementation: EC2, RDS, EBS · Production SAP S/4HANA · SOC 2 Type II ### Who implements the recommendations? Most tools hand you a list and wish you luck. The reason those lists sit untouched for months is rarely disagreement. Right-sizing an instance means stopping and starting it, and nobody wants to open a change window purely to save money. Sage removes that problem by attaching the change to a window you already run. 1 #### Sage recommends A right-sizing run produces the proposed instance type change, with the resource, account and region attached. 2 #### An action is scheduled The change becomes a tracked action, set to apply on your next OS patch. The downtime is already budgeted, so the resize costs nothing extra in risk or coordination. 3 #### Guardian executes it skie Guardian carries out the change during the patch window, alongside the maintenance work you were doing anyway. 4 #### The outcome comes back The result is written onto the recommendation, with timestamps and a status. Applied, pending and cancelled actions are counted, and failures stay visible rather than disappearing. Every action leaves a record. This one was raised on 13 August and completed on 15 August. It was not run on demand, it waited for the account's patch window. The account name is redacted here because the estate belongs to a customer; nothing else in this record has been altered. Automated implementation is available today for EC2, RDS and EBS. The estates it runs against include production SAP S/4HANA and HANA database instances. Sage is one half of a pair. The other is Guardian, which runs the estate day to day and applies these changes as part of that operation, which is why the recommendations on this page turn into results rather than backlog. See how Guardian operates the estate → ### Why optimize EC2, ECS and EKS together? Savings Plans and Reserved Instances are shared across EC2, ECS and EKS. Optimize any one of them on its own and you get the other two wrong. Sage solves all three in one run, so the commitment plan reflects the footprint you will actually have after right-sizing rather than the one you have today. What that looks like in a single optimization run What the run does Why it matters Cuts right-sizing spend and raises commitment spend in the same plan Right-sizing changes the shape of your usage, so the correct commitment position changes with it. Sometimes the cheapest total means buying more commitment, not less. A tool that treats the two separately cannot reach that answer. Scales one node group up while emptying others Consolidating workloads onto fewer, better matched node groups can mean growing one of them. The total falls even though a line item rises. Flags Reserved Instances and Savings Plans as over-committed Sage tells you when you are committed beyond the usage you can cover, reconciling every individual commitment against its measured monthly utilization. It is the opposite of the usual incentive to sell you more commitment. Treats EKS as a fleet Node groups are sized on nodes, cores and memory together, rather than one instance at a time, which is the only way the numbers come out right for Kubernetes. Carries reservation coverage next to every proposal A resize is never recommended without showing what the current instance is covered by. This is the disconnected-decision problem that raises costs when tools work in isolation. One run, four cost components. Right-sizing takes the EKS node line down while savings plan spend goes up, because the correct commitment position changed with the footprint. The total falls even though two line items rise. Sage tells you when you have committed too much. Both positions here are flagged as over-committed against measured usage, reconciled commitment by commitment. Some things grow so the total shrinks. One node group goes from one node to two while two self-managed groups empty out entirely. Node groups the optimizer chose not to touch stay in the list, labelled as no change. Kubernetes is sized as a fleet. Nodes, cores and memory move together, rather than one instance at a time. ### Does this only cover compute? No. Databases and storage are optimized the same way, and they carry their own commitment instruments rather than borrowing the compute model. A database run reaches across RDS, Aurora and DynamoDB in a single pass, weighing reserved instances and database savings plans against the sizing changes it is proposing at the same time. One run, five cost components. On-demand database spend falls while reserved instance and savings plan spend rises to meet it, DynamoDB on-demand goes to zero, and Aurora storage is left alone because there is nothing to gain there. The same trade-off as compute, with database-specific instruments. Right-sizing is not only shrinking things. Older instance families come down while newer generation families, including the ARM-based ones, go up. Most of the saving here comes from moving databases onto better hardware rather than from running them smaller. ### How do you know where you stand? Two measures, and they answer different questions. One tells you how well sized your compute is. The other tells you how well covered that compute is, and what you are paying for and not using. Both are tracked continuously, so the answer is a number rather than an opinion. #### Are the machines the right size? Raw CPU percentages are misleading across a mixed fleet, because a busy small instance and an idle large one do not carry equal weight. Sage normalizes for that: every instance type is weighted by its relative compute capacity, then utilization is measured at the p90 to p99 range rather than the average, so short peaks are not hidden by long quiet periods. The result is one figure for the whole estate. #### Is that compute actually covered? Right-sizing changes what you need to cover, so coverage has to be read against the sized footprint rather than the original one. Alongside it, Sage tracks the commitments you are paying for and not using, and when your existing commitments expire. A right-sizing score for the whole estate. Percentiles for all connected accounts, the previous month alongside it for comparison, and six months of trend so you can see whether sizing discipline is holding or drifting. Waste and the expiry cliff, tracked monthly. The top row is commitment you are paying for and not using, in dollars and as a share of spend. The bottom row is when your existing reserved instances and savings plans run out, which is what determines when the next buying decision falls due. ### What do you actually get? Three concrete outputs from every run, all exportable. #### Right-sizing recommendations Per resource: the current and proposed instance type, the account and region, an efficiency score before and after, and the commitment coverage already attached to it. Resources that should be left alone are labelled as such rather than quietly dropped. #### A commitment buy plan Line items you can execute: region, instance family, platform, term, payment option, hourly rate and annual cost, split across Instance Savings Plans, Compute Savings Plans and Convertible Reserved Instances. #### An honest disruption estimate How many instances would change family or vCPU count if you acted on the plan. The cost of acting is stated up front, not discovered afterwards. ### How the commitment optimizer works Sage plans commitments as a portfolio over a three-year horizon, quarter by quarter, using mixed-integer linear programming. It is a solver, not a set of rules of thumb, which is why it can trade instruments off against each other instead of applying them one at a time. Step What happens Existing commitments first Reserved Instances and Savings Plans you already hold are modelled as fixed coverage that expires on its own schedule. Everything Sage proposes is incremental on top of what you own. Coverage quarter by quarter Demand is covered across twelve quarters. Each quarter shows which source covers what, and what is left uncovered at on-demand rates. Three priced scenarios What you would pay with no commitments at all, what your current mix costs, and what the optimized plan costs. Savings are always quoted against a stated baseline rather than as a floating percentage. Versioned runs Every run is kept as a dated snapshot, so plans can be compared over time and a decision can be traced back to the state it was made in. Savings always have a stated baseline. Three scenarios priced over three years, with the actionable delta against your current mix reported separately from the total upside against holding no commitments at all. ### Which AWS services does Sage cover? Sage analyzes and optimizes a focused set of services, and gives you visibility across a much broader estate. The distinction is deliberate. Deep optimization is applied where it pays for itself. Service Depth What Sage does EC2 Optimized Right-sizing and re-typing, commitment planning, automated implementation ECS Optimized Included in the shared compute commitment solve EKS Optimized Node group fleet sizing across nodes, cores and memory, plus the shared commitment solve RDS Optimized Right-sizing, reserved instance planning, automated implementation EBS Optimized Volume type and size optimization, automated implementation ElastiCache Optimized Right-sizing and reserved node planning OpenSearch Optimized Right-sizing and reserved instance planning S3, EFS, FSx Visibility Inventory, spend attribution and reporting DynamoDB, Redshift, Timestream Visibility Inventory, spend attribution and reporting Load balancers, NAT gateways, elastic IPs Visibility Inventory and spend attribution, with idle resource reporting The visibility layer Sage works on top of. Total, on-demand and committed spend with month on month movement, the previous month broken down by service, and eighteen months of history by service, region and account. Optimization decisions are only as good as the spend picture underneath them. ### What happens after the spend comes down? Optimization is not the end state. It is what creates the room to build. Customer arc #### Growth that lands on an optimized baseline One customer's AWS consumption fell from roughly $44K a month to $29K after the first optimization pass, then grew to $126K as they expanded onto the platform, with no additional cloud operations headcount. The growth landed on an optimized baseline rather than an inherited one, so the new workloads were sized correctly from the day they were built. See how this works for growing teams → Monthly AWS consumption for a single customer estate. Figures rounded. Consumption growth reflects new workloads the customer chose to build; it is reported alongside the optimization work, not attributed to it. ### Before you ask What does a first optimization pass typically find? A right-sizing recommendation for every resource in scope, a commitment plan solved against the post-right-sizing footprint, and a stated count of how many instances would change. Savings are quoted against one of three priced scenarios rather than as a percentage. The assessment is free and runs against your real usage. Will it recommend resizing something that is under a reservation? Every right-sizing proposal shows the commitment coverage on the resource it applies to, and the commitment plan is solved against the post-right-sizing footprint. That connection is the point of running a single pass across compute rather than optimizing each service in isolation. Does Sage make changes to my infrastructure automatically? Only where you allow it, and only inside a maintenance window you control. Changes for EC2, RDS and EBS become scheduled actions that Guardian applies during your next patch window. Every action keeps a record showing what changed, when, and whether it succeeded. Can Sage tell me I have bought too much commitment? Yes. Reserved Instances and Savings Plans are reconciled against measured utilization, and over-committed positions are flagged. Every individual commitment is listed with its monthly commitment against its monthly usage. Does it work with Kubernetes? EKS node groups are sized as a fleet, across node count, cores and memory together, and EKS shares the compute commitment solve with EC2 and ECS. What baseline are savings measured against? Sage prices three scenarios: no commitments at all, your current mix, and the optimized plan. Savings are always stated against one of those rather than as an unattributed percentage. How often does it run? Optimizations can be run on demand or in batches, and each run is stored as a dated snapshot so plans can be compared over time. Analyses older than thirty days are flagged as stale. Can we buy this through AWS Marketplace? Yes. Sage, Guardian and Muse are all transactable through AWS Marketplace, so spend applies against your existing AWS commitment. ### Customer results AWS cost optimization skie has delivered with Sage #### Liaison Education Higher education technology company. skie cut its AWS costs by $1.8M a year while keeping performance and scalability. Read the Liaison Education case study → #### Cognitus SAP consultancy. $400K a year saved on cloud costs while improving security and scalability as the business grew. Read the Cognitus case study → ### See what Sage finds in your account A free assessment runs against your real usage and returns the same right-sizing recommendations, commitment plan and disruption estimate described on this page. --- # Sage for Azure: Cost Optimization & Managed FinOps | skie.io URL: https://skie.io/products/sage/azure/ Description: Sage right-sizes Azure VMs, SQL databases and managed disks and decides reservations in the same run, so sizing and commitments never conflict. Sage for Azure ## Right-sizing and reservations, decided together Sage is skie's cost optimization engine for Azure. Every run returns two things at once: how your resources should be sized, and which reservations to hold. They are produced together because deciding either one on its own gets the other wrong. Sage customers typically cut cloud spend by 25-50%. Across the whole customer base running Sage through 2025, realised spend fell 41% on average against the pre-optimization run rate. - Trusted & qualified - AWS Advanced Tier Partner - AWS Well-Architected - SAP Competency - SOC 2 Type II ### One run, two kinds of decision Optimization tools usually separate right-sizing from commitment buying, which is how you end up being told to resize a virtual machine that a reservation is already paying for. A Sage run on Azure returns both sets of recommendations side by side, and each proposed resize carries the reservation coverage of the machine it applies to. - Output 1 #### Right-sizing Per machine: current size and proposed size, subscription, location, and an efficiency score before and after. - Output 2 #### Reservation decisions Which commitments to add, shown as new purchases by region and series, priced against the post-resize footprint. - Shared #### Coverage on every row Each resize proposal shows what the machine is currently covered by, so a change is never recommended blind to the commitment paying for it. - Shared #### What to leave alone Resources the optimizer decided not to touch are labelled explicitly rather than dropped from the list, so you can see the whole estate was considered. Coverage sits next to every proposal. The right-hand column shows what each machine is already covered by, so a resize is never suggested blind to the reservation paying for it. Machines the optimizer decided to leave alone stay in the list, labelled as no change. The subscription column has been removed from this image because those names identify the customer; nothing else has been altered. ### What a run gives you Every optimization reports the cost position before and after, split by how you are paying rather than only by how much. Reported Detail Projected savings Monthly and annualised, with the percentage change against current spend Cost split by commitment type Current and optimized monthly cost broken into reserved and pay-as-you-go, so you can see spend moving from one to the other rather than only the net figure Actionable count How many of the analyzed resources have a recommendation worth acting on, against the total examined Reservation flow Where new commitment would go, by region and machine series Run history Every run stored and dated, with analyses older than thirty days flagged as stale, plus CSV export of any recommendation set The split matters as much as the total. Pay-as-you-go spend moves onto reservations rather than simply disappearing, so you can see where the saving comes from instead of taking the net figure on trust. Where the new commitment actually goes. One decision resolves into seven specific purchases, each tied to a region and a machine series, with the total commitment it puts on the books stated plainly rather than buried. Right-sizing falls, commitment rises, and some series grow. The plan spends more on reservations because right-sizing changed what needs covering, and it grows two machine series while shrinking two others. The total drops even though several lines move up. ### Which Azure services does Sage cover? Optimization runs against virtual machines, SQL databases and managed disks. Visibility and spend attribution extend across the wider estate. Service Depth What Sage does Virtual machines Right-sizing with efficiency scoring, plus reservation decisions in the same run SQL databases Right-sizing and reservation planning, including managed instances Managed disks Disk sizing and tier optimization, with snapshot inventory VM scale sets Inventory of scale sets and their instances, with spend attribution Reservations and savings plans Full inventory of what you hold, with reservation spend reporting Storage accounts, load balancers Inventory and spend attribution ### Understanding where Azure spend goes Azure billing is reported by meter rather than by service, which is a large part of why Azure costs are harder to attribute than AWS costs. Sage reports spend the way Azure actually bills it, then adds the views you need on top. By meter Spend by meter category and sub category, over eighteen months By location Regional spend distribution across subscriptions By group Resource group attribution for chargeback and showback By commitment A separate reservation spend view, split from on-demand Reported the way Azure bills. Total spend split between on-demand and commitment, then the month broken down by meter category rather than forced into a service taxonomy that Azure does not actually use. ### Common questions Will Sage tell me to resize a machine that a reservation already covers? No. Every right-sizing row carries the reservation coverage of the machine it applies to, and reservation decisions are produced in the same run as the sizing recommendations rather than separately. How does Sage decide what to leave alone? Resources the optimizer examined but chose not to change are labelled as no change, and stay in the list. You can see the whole estate was considered rather than only the parts with a recommendation attached. What is an efficiency score? A measure of how well a machine's size matches its actual workload, reported before and after the proposed change so the improvement is visible per resource. Can I export the recommendations? Yes. Any recommendation set can be exported to CSV, and every run is stored with its date so plans can be compared over time. How does this differ from Sage for AWS? The AWS product solves commitments across EC2, ECS and EKS in one portfolio, because Savings Plans are shared between them, and it can apply approved changes for you during a patch window. On Azure, right-sizing and reservation decisions are produced together per service. See Sage for AWS for that side of the platform. ### Customer results Cloud cost optimization skie has delivered with Sage #### Liaison Education Higher education technology company. skie cut its AWS costs by $1.8M a year while keeping performance and scalability. Read the Liaison Education case study → #### Cognitus SAP consultancy. $400K a year saved on cloud costs while improving security and scalability as the business grew. Read the Cognitus case study → ### See what Sage finds in your subscriptions A free assessment runs against your real usage and returns the same right-sizing recommendations and reservation decisions described on this page. --- # Sage: Intelligent Cloud Cost Optimization | skie.io URL: https://skie.io/products/sage/ Description: Cut cloud costs by 25-50% using sophisticated algorithms and our team of experts. Zero-risk model: Pay only on achieved savings. ## Your always-optimized cloud starts here Sage combines FinOps automation and expert guidance to cut cloud costs by 25-50%, across AWS and Azure. It plans right-sizing and commitments together, because changing one changes the answer to the other. Zero-risk model: pay only on achieved savings. Available through AWS Marketplace. AWS Advanced Tier Partner · Microsoft Solutions Partner · Available on AWS Marketplace · SOC 2 Type II ### The connected intelligence difference Most cloud optimization tools work in silos. Each recommendation looks sensible on its own, but disconnected decisions can actually increase costs or break critical systems. #### A resize that fights your reservation A tool tells you to right-size an instance without realizing you hold a 3-year reservation for the current type. #### A family switch that breaks SAP Or it recommends switching instance families without checking whether your SAP HANA workload is certified for the new type. Sage is different. We see the complete picture. ### Who implements the recommendations? Most tools hand you a list and wish you luck. The reason those lists sit untouched for months is rarely disagreement. Right-sizing an instance means stopping and starting it, and nobody wants to open a change window purely to save money. Sage removes that problem by attaching the change to a window you already run. 1 #### Sage recommends A right-sizing run produces the proposed instance type change, with the resource, account and region attached. 2 #### An action is scheduled The change becomes a tracked action, set to apply on your next OS patch. The downtime is already budgeted, so the resize costs nothing extra in risk or coordination. 3 #### Guardian executes it skie Guardian carries out the change during the patch window, alongside the maintenance work you were doing anyway. 4 #### The outcome comes back The result is written onto the recommendation, with timestamps and a status. Applied, pending and cancelled actions are counted, and failures stay visible rather than disappearing. Every action leaves a record. This one was raised on 13 August and completed on 15 August. It was not run on demand, it waited for the account's patch window. The account name is redacted here because the estate belongs to a customer; nothing else in this record has been altered. Automated implementation is available today for EC2, RDS and EBS. The estates it runs against include production SAP S/4HANA and HANA database instances. Sage is one half of a pair. The other is Guardian, which runs the estate day to day and applies these changes as part of that operation, which is why the recommendations on this page turn into results rather than backlog. See how Guardian operates the estate → ### Why optimize EC2, ECS and EKS together? Savings Plans and Reserved Instances are shared across EC2, ECS and EKS. Optimize any one of them on its own and you get the other two wrong. Sage solves all three in one run, so the commitment plan reflects the footprint you will actually have after right-sizing rather than the one you have today. What that looks like in a single optimization run What the run does Why it matters Cuts right-sizing spend and raises commitment spend in the same plan Right-sizing changes the shape of your usage, so the correct commitment position changes with it. Sometimes the cheapest total means buying more commitment, not less. A tool that treats the two separately cannot reach that answer. Scales one node group up while emptying others Consolidating workloads onto fewer, better matched node groups can mean growing one of them. The total falls even though a line item rises. Flags Reserved Instances and Savings Plans as over-committed Sage tells you when you are committed beyond the usage you can cover, reconciling every individual commitment against its measured monthly utilization. It is the opposite of the usual incentive to sell you more commitment. Treats EKS as a fleet Node groups are sized on nodes, cores and memory together, rather than one instance at a time, which is the only way the numbers come out right for Kubernetes. Carries reservation coverage next to every proposal A resize is never recommended without showing what the current instance is covered by. This is the disconnected-decision problem that raises costs when tools work in isolation. One run, four cost components. Right-sizing takes the EKS node line down while savings plan spend goes up, because the correct commitment position changed with the footprint. The total falls even though two line items rise. Sage tells you when you have committed too much. Both positions here are flagged as over-committed against measured usage, reconciled commitment by commitment. Some things grow so the total shrinks. One node group goes from one node to two while two self-managed groups empty out entirely. Node groups the optimizer chose not to touch stay in the list, labelled as no change. Kubernetes is sized as a fleet. Nodes, cores and memory move together, rather than one instance at a time. ### Does this only cover compute? No. Databases and storage are optimized the same way, and they carry their own commitment instruments rather than borrowing the compute model. A database run reaches across RDS, Aurora and DynamoDB in a single pass, weighing reserved instances and database savings plans against the sizing changes it is proposing at the same time. One run, five cost components. On-demand database spend falls while reserved instance and savings plan spend rises to meet it, DynamoDB on-demand goes to zero, and Aurora storage is left alone because there is nothing to gain there. The same trade-off as compute, with database-specific instruments. Right-sizing is not only shrinking things. Older instance families come down while newer generation families, including the ARM-based ones, go up. Most of the saving here comes from moving databases onto better hardware rather than from running them smaller. ### How do you know where you stand? Two measures, and they answer different questions. One tells you how well sized your compute is. The other tells you how well covered that compute is, and what you are paying for and not using. Both are tracked continuously, so the answer is a number rather than an opinion. #### Are the machines the right size? Raw CPU percentages are misleading across a mixed fleet, because a busy small instance and an idle large one do not carry equal weight. Sage normalizes for that: every instance type is weighted by its relative compute capacity, then utilization is measured at the p90 to p99 range rather than the average, so short peaks are not hidden by long quiet periods. The result is one figure for the whole estate. #### Is that compute actually covered? Right-sizing changes what you need to cover, so coverage has to be read against the sized footprint rather than the original one. Alongside it, Sage tracks the commitments you are paying for and not using, and when your existing commitments expire. A right-sizing score for the whole estate. Percentiles for all connected accounts, the previous month alongside it for comparison, and six months of trend so you can see whether sizing discipline is holding or drifting. Waste and the expiry cliff, tracked monthly. The top row is commitment you are paying for and not using, in dollars and as a share of spend. The bottom row is when your existing reserved instances and savings plans run out, which is what determines when the next buying decision falls due. ### Turn complex cloud commitments into maximum savings Before #### Commitments bought one at a time 40% 25% 35% - On-demand, no discount - 1-year commitment, 30% off - 3-year commitment, 50% off 25% blended discount, typical for self-managed Sage weighs #### Six signals, continuously - Hourly usage patterns - Spend forecast - License implications - Right-sizing headroom - Standard or Convertible - Term laddering Rebalanced monthly After #### A layered commitment portfolio Instrument Coverage Discount Standard RI + RI Volume Discount 20% 75% Instance Savings Plans 25% 70% Compute Savings Plans 30% 50% Convertible RIs 25% 50% 60% blended discount across a 3-year ladder While others guess at commitments, Sage orchestrates a sophisticated portfolio strategy that achieves a 60% blended discount vs. the typical 25%. Modelled example. The coverage split and discount depths shown are AWS list rates for a representative estate; your blended result depends on your own usage mix. ### How Sage builds your optimal commitment portfolio 1 #### Deep usage analysis - Hourly usage patterns across all instances - Workload migration opportunities (Graviton, modernization) - Spot instance eligibility assessment 2 #### Commitment strategy design - Instance family standardization recommendations - Multi-year discount optimization - Regional vs. global coverage decisions 3 #### Portfolio construction - Layer commitments for maximum coverage - Balance flexibility with discount depth - Account for volume discounts and spreading options 4 #### Continuous optimization - Monitor utilization in real-time - Adjust for workload changes - Maximize new instance family opportunities #### A 60% blended discount vs. 25% from typical approaches And the flexibility others can't offer. Capability Basic approach Sage portfolio Blended discount 25% 60% Considers hourly patterns ✕ ✓ Instance standardization ✕ ✓ Commitment flexibility ✕ ✓ Spreading capability ✕ ✓ Expert review ✕ ✓ "Sage didn't just save us money—it transformed how we think about cloud commitments. The portfolio approach delivered 58% savings while actually reducing our risk." VP Infrastructure, Fortune 500 Company ### What do you actually get? Three concrete outputs from every run, all exportable. #### Right-sizing recommendations Per resource: the current and proposed instance type, the account and region, an efficiency score before and after, and the commitment coverage already attached to it. Resources that should be left alone are labelled as such rather than quietly dropped. #### A commitment buy plan Line items you can execute: region, instance family, platform, term, payment option, hourly rate and annual cost, split across Instance Savings Plans, Compute Savings Plans and Convertible Reserved Instances. #### An honest disruption estimate How many instances would change family or vCPU count if you acted on the plan. The cost of acting is stated up front, not discovered afterwards. ### What Sage optimizes Comprehensive optimization across all major cloud services, on AWS and Azure. #### Compute - AWS EC2 Instances - AWS Compute Reservations and Savings Plans - AWS Container Services (ECS/EKS) - Azure Virtual Machines - Azure Scaling Sets - Azure Virtual Machine Reservations and Savings Plans - Licensing (Microsoft, Oracle and SUSE) #### Storage - AWS S3 Buckets & Lifecycle - AWS EBS Volumes - AWS EFS & FSx - Azure Managed Disks - Azure Files - Azure Blobs - Azure Storage Reservations - Backup & Archives #### Databases - AWS RDS Instances - AWS ElastiCache, Redis and Valkey - AWS OpenSearch - Azure SQL Database - Azure SQL Managed Instance - Azure Database for PostgreSQL - Database Reservations #### And more - AWS Load Balancers - AWS Elastic IP - Commitment Portfolio Management - Automation: apply recommendations On AWS, depth by service Service Depth What Sage does EC2 Optimized Right-sizing and re-typing, commitment planning, automated implementation ECS Optimized Included in the shared compute commitment solve EKS Optimized Node group fleet sizing across nodes, cores and memory, plus the shared commitment solve RDS Optimized Right-sizing, reserved instance planning, automated implementation EBS Optimized Volume type and size optimization, automated implementation ElastiCache Optimized Right-sizing and reserved node planning OpenSearch Optimized Right-sizing and reserved instance planning S3, EFS, FSx Visibility Inventory, spend attribution and reporting DynamoDB, Redshift, Timestream Visibility Inventory, spend attribution and reporting Load balancers, NAT gateways, elastic IPs Visibility Inventory and spend attribution, with idle resource reporting The visibility layer Sage works on top of. Total, on-demand and committed spend with month on month movement, the previous month broken down by service, and eighteen months of history by service, region and account. Optimization decisions are only as good as the spend picture underneath them. Go deeper on your cloud Sage for AWS EC2, ECS, EKS, RDS and more Right-sizing and commitment planning solved in one pass, with approved changes applied inside your patch window. See Sage for AWS → Sage for Azure Virtual machines, SQL and disks Sizing and reservation decisions produced together, so a resize is never recommended blind to the commitment paying for it. See Sage for Azure → ### Savings you can count on, with no risk #### 25-50% cost reduction Our proven optimization strategies consistently deliver 25-50% cost savings. Most clients see results in their first 90 days. Across all our customer base running Sage through 2025, realised cloud spend fell 41% on average against their pre-optimization run rate. #### Zero risk model Pay only on achieved savings. No upfront costs, no monthly fees. If we don't save you money, you don't pay us anything. #### Expert support Dedicated team of certified FinOps and DevOps experts guide you through every optimization, ensuring successful implementation. #### Multi-cloud ready Optimize across AWS and Azure with a single platform. Get unified visibility and optimization recommendations for your entire cloud estate. #### Automated execution Get recommendations and implement them automatically. Our platform integrates with your tools for continuous optimization. #### Continuous optimization Cloud usage changes constantly. Sage continuously monitors and optimizes, ensuring you're always running at peak efficiency and at the lowest cost. #### Proven results Navisite: "We saved $3M annually in just 7 months." Join hundreds of companies achieving similar transformational savings. #### Enterprise ready Handle complex multi-account environments, standardize instance families, and manage commitments across your entire organization. ### What happens after the spend comes down? Optimization is not the end state. It is what creates the room to build. Customer arc #### Growth that lands on an optimized baseline One customer's AWS consumption fell from roughly $44K a month to $29K after the first optimization pass, then grew to $126K as they expanded onto the platform, with no additional cloud operations headcount. The growth landed on an optimized baseline rather than an inherited one, so the new workloads were sized correctly from the day they were built. See how this works for growing teams → Monthly AWS consumption for a single customer estate. Figures rounded. Consumption growth reflects new workloads the customer chose to build; it is reported alongside the optimization work, not attributed to it. ### Customer results Cloud cost optimization skie has delivered with Sage #### Liaison Education Higher education technology company. skie cut its AWS costs by $1.8M a year while keeping performance and scalability. Read the Liaison Education case study → #### Cognitus SAP consultancy. $400K a year saved on cloud costs while improving security and scalability as the business grew. Read the Cognitus case study → ### Before you ask What does a first optimization pass typically find? A right-sizing recommendation for every resource in scope, a commitment plan solved against the post-right-sizing footprint, and a stated count of how many instances would change. Savings are quoted against one of three priced scenarios rather than as a percentage. The assessment is free and runs against your real usage. Will it recommend resizing something that is under a reservation? Every right-sizing proposal shows the commitment coverage on the resource it applies to, and the commitment plan is solved against the post-right-sizing footprint. That connection is the point of running a single pass across compute rather than optimizing each service in isolation. Does Sage make changes to my infrastructure automatically? Only where you allow it, and only inside a maintenance window you control. Changes for EC2, RDS and EBS become scheduled actions that Guardian applies during your next patch window. Every action keeps a record showing what changed, when, and whether it succeeded. Can Sage tell me I have bought too much commitment? Yes. Reserved Instances and Savings Plans are reconciled against measured utilization, and over-committed positions are flagged. Every individual commitment is listed with its monthly commitment against its monthly usage. Does it work with Kubernetes? EKS node groups are sized as a fleet, across node count, cores and memory together, and EKS shares the compute commitment solve with EC2 and ECS. What baseline are savings measured against? Sage prices three scenarios: no commitments at all, your current mix, and the optimized plan. Savings are always stated against one of those rather than as an unattributed percentage. How often does it run? Optimizations can be run on demand or in batches, and each run is stored as a dated snapshot so plans can be compared over time. Analyses older than thirty days are flagged as stale. Can we buy this through AWS Marketplace? Yes. Sage, Guardian and Muse are all transactable through AWS Marketplace, so spend applies against your existing AWS commitment. ### Discover your savings potential Get a detailed report showing exactly how much you can save and where the opportunities lie. No commitment required. - AI-powered analysis of your entire cloud infrastructure - Detailed savings projections by service and resource - Risk-free recommendations with implementation roadmap - Results delivered in 2 weeks or less - No access to sensitive data required - Read-only access to cost and usage reports - White-glove service from cloud experts - 100% free with no obligations Perfect for organizations with growing cloud spend 1 #### Quick setup Provide read-only access to your AWS Cost Explorer or Azure Cost Management. We handle the rest with zero disruption to your operations. 2 #### Deep analysis Sage analyzes your usage patterns, identifies inefficiencies, and calculates savings opportunities across all services and resources. 3 #### Detailed report Receive comprehensive recommendations, projected savings, and a clear implementation roadmap you can execute immediately. ### See what Sage finds in your account A free assessment runs against your real usage and shows exactly where the savings are, with a roadmap you can act on. You only pay on savings achieved. --- # skie.io Platform Release Notes | skie.io URL: https://skie.io/release-notes/ Description: Everything new in the skie.io platform: month-by-month product updates covering cost optimization, Guardian managed services, Kubernetes visibility, and more. ## Here's everything that's new. ### July 2026 Latest #### Maintenance Windows Define maintenance windows in your own time zone and the platform schedules optimization changes to execute only inside them. Automated savings, on your schedule. #### Rebuilt EC2 & RDS Optimization Pages Our two flagship optimization experiences were redesigned from the ground up with new dashboards, clearer recommendations, and faster navigation. #### CSV Export Everywhere A new export experience across EC2, RDS, EKS, and other inventory and cost pages, so any view can leave the platform as a spreadsheet. #### Multi-Select Filters Data-source and account filters now support multi-select, making cross-account analysis effortless. #### Networking Inventory Client VPN and NAT Gateways (including regional NAT gateways) join the platform's inventory, alongside new Aurora pricing data. #### Jira Ticket Templates Customize the Jira tickets created for change requests with configurable templates that match your ITSM conventions. ### June 2026 #### AWS Score Dashboard A new scoring dashboard that rates your AWS posture at a glance, with account filters, savings-plan scoring, and member-account scoping. #### Redesigned Navigation A rebuilt sidebar with search, recent items, and pinnable sections, plus consistent breadcrumbs and page headers across the whole app. #### Smart Filter Bar Filter any table faster with a smart filter bar that auto-selects the right operators as you type. #### Jira-Driven OS Patching OS patch scheduling is now integrated with Jira tickets end to end, with robust failure handling and per-customer toggles. #### Service Desk Refinements Service-desk reporting now separates customer-reported from monitoring-reported tickets, shows all tickets, and keeps your filters as you navigate. #### HPA Recommendations The platform now recommends Kubernetes Horizontal Pod Autoscaler settings, alongside new safety guardrails that prevent actions on deleted clusters. ### May 2026 #### Jira Change-Request Automation Change requests now create and update Jira tickets automatically (summaries, comments, request types, and per-issue filters), closing the loop with your ITSM. #### Database Savings Plans Recommendations Purchase recommendations for Database Savings Plans, so commitment coverage now spans compute and databases alike. #### AWS Portfolio View & Opportunity Board A portfolio-level AWS overview plus a heatmap and opportunity board for spotting your biggest savings opportunities at a glance. #### SQL Server License Compliance Track SQL Server licenses across your fleet with a dedicated licensing-compliance view. #### Pre-Approved & Scheduled Changes Change requests gained a pre-approval option and Guardian-managed scheduling, with improved monitoring of in-flight changes. #### Azure Cost Analysis & DB Optimizer A new Azure VM Cost Analysis report and a bin-packing option for the Azure database optimizer. ### April 2026 #### Optimizations via Change Requests EC2 and EBS optimization actions now execute through an approval-based change-request workflow, with cancellation handling and clear error reporting. #### CrowdStrike Integration CrowdStrike hosts and detections flow into the platform, with detection trend charts and drill-down views in the Guardian security dashboard. #### Aurora Serverless Support ACU utilization metrics and charts for serverless Aurora and RDS, now included in RDS Cost Analysis. #### Trends, Exports & Permissions Trend charts across security, backup, and patching, CSV exports, reporter-type filters, and granular dashboard permissions. #### Per-Organization Optimizer Settings Tune optimizer behavior (cutoff dates, targets, and more) per organization, so recommendations fit each customer's operating model. #### OS Inventory Operating-system and image details tracked for EC2 instances and Azure VMs, including last-installed-package dates for patch insight. ### March 2026 #### Backup & Disaster Recovery Suite AWS Backup and restore-job tracking, Elastic Disaster Recovery coverage, and RPO/RTO compliance metrics with history. #### Commvault Integration Commvault clients and backup/restore jobs integrated into the Guardian dashboard for customers using Commvault. #### EKS Cost Dashboard & Container Right-Sizing A dedicated EKS costs dashboard, cluster capacity charts, and container CPU/memory recommendations with editable charts. #### Commitments Page A consolidated view of savings plans and reservations with monthly utilization charts. #### Complete Spanish & Portuguese Experience Every page, message, and menu translated. The platform is now fully usable in three languages. #### Free Trials on AWS Marketplace Free-trial plans and marketplace offers, with a smoother onboarding flow when subscribing through AWS Marketplace. ### February 2026 #### Guardian Dashboard v2 A brand-new managed-services dashboard: executive summary plus dedicated sections for incident response, security compliance, patch management, backup & DR, service desk, and trends. #### AWS Security Hub Findings Security Hub findings ingested and surfaced in the platform, the start of cloud security posture reporting. #### Redshift, Timestream, FSx & Neptune Four more AWS services join the platform's inventory with dedicated views and APIs. #### OS Patch & Package Inventory Installed patches and packages imported per instance, powering Guardian patch-management metrics. #### Savings-Plan Action Scheduling Schedule savings-plan optimization actions and track EC2 reservation coverage and utilization. #### One-Click CloudFormation Setup A one-click setup link and extended permissions template, so executing optimizations needs no manual IAM work. GovCloud onboarding improved too. ### January 2026 #### Multi-Language Support The platform learned Spanish and Portuguese, with per-user language settings and locale-aware dates and numbers. #### Plans, Features & Entitlements Subscription plans now gate features across the dashboard, giving every customer exactly the product they signed up for. #### Automated Marketplace Billing AWS Marketplace usage billed automatically with an approval step and improved support-fee calculations. #### EC2 CPU Capacity Dashboard See CPU capacity utilization across your EC2 fleet and get recommendations for CPU-optimized instance choices. #### AWS Backup Visibility Backup plans, coverage queries, and data-protection metrics arrive on the Guardian dashboard. #### DynamoDB Inventory DynamoDB tables tracked with dedicated views, later enriched with pricing and daily cost data. ### December 2025 #### EKS Support Lands First-class Amazon EKS support: cluster and nodegroup inventory, per-cluster settings, and high-resolution Kubernetes metrics. #### Plans & Subscriptions Groundwork The foundation for self-serve packaging: subscription management, plan templates, and AWS Marketplace product associations. #### Deletion Audit Trail A new audit system records inventory deletions (starting with EC2 instances and EBS volumes) for full traceability. #### Skie Assist on More Pages The AI assistant now helps on EBS volume recommendations and Azure compute optimization pages. #### Azure VM Right-Sizing Charts CPU utilization charts on Azure right-size recommendations, so you can validate every suggestion against real usage. #### ElastiCache Reserved Instances Commitment coverage extended with ElastiCache reserved instances and an additional savings-plan type. ### Want to see these in action? --- # AWS Migration Services: Arrive Optimized | skie.io URL: https://skie.io/services/cloud-migrations/ Description: Move to the cloud and arrive optimized. Our unique approach ensures every workload lands cost-optimized and Well-Architected from day one. ## AWS Migration Services ### Migrate Right. Optimize Forever. Move to the cloud and arrive optimized. Our unique approach integrates AWS Migration Hub with skie Sage's Connected Intelligence to ensure every workload lands cost-optimized and Well-Architected from day one. 40% Cost Reduction Built into migration planning 60% Faster Delivery AI-powered planning 100% Well-Architected AWS best practices 24/7 Always Optimized Continuous improvement ### Our 5-Phase Intelligent Migration 1 #### Discover Automated cataloging with dependency mapping AWS Application Discovery Service 2 #### Assess Optimized TCO analysis with Sage intelligence skie SAGE AWS Migration Evaluator 3 #### Plan AI-powered wave planning with Muse skie MUSE AWS Migration Hub 4 #### Migrate Block level Asynchronous Migration AWS Application Migration Service 5 #### Optimize Continuous improvement with Guardian management skie GUARDIAN ### The skie Difference #### Traditional Migrations - Lift and shift problems - Optimize later (maybe) - Surprise costs - Manual processes #### skie Migrations - Transform while migrating - Land optimized - Predictable savings - AI-powered automation ### Proven Results Consumer Food 180+ Servers Multi-Cloud On Prem and Google 6 Months Mining Company SAP S4 Hana 6 Weeks Zero Incidents ### Our Platform Advantage Identifies optimal landing zones considering costs, licenses, and dependencies Automates planning and documentation with AI Ensures continuous optimization post-migration ### Our Credentials #### AWS Cloud Operations Competency Cloud Financial Management #### Solution Provider Program Authorized to resell AWS services with integrated billing and support #### AWS Advanced Tier Partner Proven track record 100% Migration Success Rate ### Customer results Migrations skie has delivered and now operates #### JRC Mining group with operations in Peru, Mexico and Canada. SAP S/4HANA moved off VMware to AWS in four weeks, and skie has operated the environment since. Read the JRC case study → #### San Fernando Peru's largest poultry producer. skie migrated its SAP landscape from VMware to AWS and now operates it under Guardian managed services. Read the San Fernando case study → ### Get Started Option 1 #### Free Migration Assessment See your optimization potential - Current state analysis - TCO comparison - Savings projection - Migration roadmap Most Popular Option 2 #### Proof of Concept Migrate 5-10 apps risk-free - Small-scale validation - Real cost savings - Process refinement - Team training Option 3 #### Full Migration Transform your infrastructure - Complete migration - All workloads - Guaranteed SLAs - Ongoing optimization å --- # FinOps Services: Cloud Financial Management | skie.io URL: https://skie.io/services/finops-capabilities/ Description: Partner with our FinOps experts to build mature cloud financial operations that drive accountability, predictability, and strategic decision-making. ## Build world-class FinOps capabilities Transform your cloud financial management. Partner with our FinOps experts to build mature cloud financial operations that drive accountability, predictability, and strategic decision-making across your organization. AWS Advanced Tier Partner · Microsoft Solutions Partner · SOC 2 Type II ### The FinOps maturity journey Crawl #### Basic views - Basic cost visibility - Manual reporting - Reactive approach Walk #### Cost control - Automated reporting - Basic optimization - Team accountability Run #### Full FinOps - Predictive analytics - Automated optimization - Business alignment Fly #### Strategic partner - Strategic advisor - Innovation enabler - Profit center 45% Cost reduction Average improvement from Crawl to Fly stage ### Why FinOps maturity matters #### For Finance - Predictable cloud costs - Accurate forecasting - Clear ROI metrics #### For Engineering - Cost awareness in design - Budget autonomy - Performance and cost optimization #### For Executives - Strategic cloud decisions - Competitive unit economics - Cloud as profit center ### Your FinOps team extension We deliver software and embed our FinOps expertise into your organization: 1 #### Weekly FinOps reviews with your team 2 #### Custom training programs for your stakeholders 3 #### Best practices implementation tailored to your culture 4 #### Maturity roadmap development with clear milestones ### Core FinOps capabilities we build together 1 #### Cloud Cost Observability From blind spots to full visibility - Multi-dimensional cost analysis across accounts, services, tags, and teams - Drill-down dashboards from executive to engineer views - Real-time cost tracking with daily granularity - Custom cost metrics aligned to your business Outcome "Finally, everyone understands their cloud impact" 2 #### Tagging Strategy & Hygiene From chaos to governance - Tagging framework design aligned to your organization - Automated tag enforcement via policies - Tag hygiene monitoring with compliance scores - Retroactive tagging for historical analysis Outcome "100% cost attribution achieved in 90 days" 3 #### Cost Allocation & Chargeback From shared costs to clear ownership - Sophisticated allocation rules for shared resources - Business unit chargeback with full transparency - Showback reporting for cost awareness - Department-level P&L integration Outcome "Each team owns and optimizes their cloud spend" 4 #### Advanced Forecasting From surprises to predictability - ML-based forecasting learning from your patterns - Business driver correlation (users, revenue, etc.) - Scenario modeling for growth planning - Linear regression analysis for trend identification Outcome "CFO-grade cloud financial planning" 5 #### Unit Economics Reporting From total spend to business metrics - Cost per customer/revenue/transaction/API call - Multi-Cloud efficiency ratios and benchmarking - Margin impact analysis by product/feature - Business metric dashboards for executives Outcome "Cloud costs directly tied to business value" 6 #### Budgeting & Controls From reactive to proactive - Flexible budget hierarchies by team/project/environment - Automated budget alerts with escalation - Spend approval workflows for new resources - Budget vs. actual reporting with variance analysis Outcome "No more end-of-month cloud bill surprises" 7 #### Anomaly Detection From manual monitoring to AI-powered alerts - ML-powered anomaly detection learning normal patterns - Intelligent alerting reducing false positives - Root cause analysis for cost spikes - Automated remediation for common issues Outcome "Catch cost anomalies before they become problems" ### The Sage FinOps platform features #### Unified FinOps Dashboard - Multi-Cloud Cost Visibility - Cost Categories (Product, Project, Team, etc.) - Waste and Savings opportunities - Forecasting #### Policy Engine - Tagging enforcement - Budget controls - Approval workflows - Compliance tracking #### Integration Hub - Rich API - ITSM platforms - BI tools - Slack/Teams alerts Explore Sage → ### FinOps maturity assessment Your score 75% - Crawl 0-25% - Walk 26-50% - Run 51-75% - Fly 76-100% "See where you are today and where Sage will take you" Start your FinOps journey with a personalized assessment. ### "From Chaos to Control in 6 Months" Challenge: Global retailer with no cost visibility across 500+ AWS accounts #### Results - 100% cost accountability - 35% reduction in "shadow IT" cloud spend - 3-day close for cloud financials (was 15 days) - Board-level cloud ROI reporting #### Journey with Sage - Month 1 Implemented tagging strategy - Month 2 Achieved 95% cost attribution - Month 3 Launched team chargebacks - Month 4 Deployed unit cost metrics - Month 5 Enabled predictive budgeting - Month 6 Full FinOps maturity achieved ### Customer results FinOps outcomes skie has delivered for customers #### Liaison Education Higher education technology company. skie cut its AWS costs by $1.8M a year while keeping performance and scalability. Read the Liaison Education case study → #### Cognitus SAP consultancy. $400K a year saved on cloud costs while improving security and scalability as the business grew. Read the Cognitus case study → ### Ready to mature your FinOps practice? Get a free FinOps maturity assessment and roadmap. --- # VMware Exit and SAP Modernization on AWS | skie.io URL: https://skie.io/services/vmware-sap/ Description: Your VMware renewal and the SAP ECC 2027 deadline are one decision. skie.io assesses, migrates and runs SAP and its estate on AWS, across four paths. ## Two clocks are running. Make one decision, not two. Broadcom's per-core VMware pricing at your next renewal and the end of ECC maintenance (2027, extended 2030) point at the same decision. skie.io runs it as one program: assess the estate, choose the S/4 path on evidence, move SAP and everything around it to AWS, and operate it from day one. 8 months SAP + 40 applications off VMware 4 weeks S/4HANA moved off VMware 6 months ECC-to-S/4HANA brownfield AWS Advanced Tier Partner · AWS SAP Services Competency · AWS Cloud Operations Competency · SOC 2 Type II ### Your VMware renewal and your SAP 2027 deadline are the same question. Both force a decision about where SAP and the systems around it will run for the next decade. Answering them separately means paying for the transition twice. At your next renewal #### VMware under Broadcom - Perpetual licenses are gone; subscription only - Thousands of SKUs collapsed into a handful of bundles, so you pay for capabilities you may not use - Billing moved to per-core, with minimums - Renewal quotes commonly come in at two to three times the previous term; Gartner puts the typical increase at 300-400% - The servers under it are due as well: replacement hardware is quoting 1.5-2× the last cycle on memory shortages The renewal date is your decision deadline for the whole virtualized estate, from the hypervisor up. It applies whether that estate is in your data center or in a hosting provider's. + End of 2027 #### SAP ECC mainstream maintenance - Mainstream maintenance for ECC 6.0 ends in 2027; extended maintenance runs to 2030 at a premium, with limited options beyond - Every path forward, whether you keep the infrastructure or SAP runs it, starts with a new landing - An SAP move is the largest, most sensitive workload in the VMware estate Choosing where S/4 will run answers the VMware question for SAP's whole dependency chain. So instead of a VMware project and an SAP project One assessment. One landing on AWS. One program that finishes optimized. ### SAP never moves alone. The databases, integration, identity, custom applications, file services and backup around SAP are what make the migration hard. We scope, sequence and move the whole cluster under one dependency map, one wave plan and one team, so cutover does not leave you with a hybrid estate and two operating models. SAP ECC or S/4HANA Databases HANA, SQL Server, Oracle Integration PI/PO, middleware, EDI Identity & network AD, DNS, firewalls Custom & ISV apps Portals, WMS, MES Files & print Shares, archives, output Backup & DR Retention, recovery ### We do not sell one answer. The assessment compares all four against your estate, your timeline and your budget, and shows the trade-offs before you commit. SAP's own cloud editions are on the table as real options, not defaults; so is keeping SAP on infrastructure you own. You own the SAP infrastructure Runs in your AWS account. skie.io migrates it and operates it. A #### Move ECC to AWS now, convert later Migrate ECC and its cluster to AWS ahead of the renewal, then run the S/4 conversion on AWS when the business is ready. Fits when The VMware clock is the tighter one and the S/4 roadmap is still being decided Watch for Two change windows instead of one; ECC on AWS still needs a conversion plan before maintenance ends This is the path a large vertically integrated food producer took: ECC and 40+ applications to AWS in one cutover, SAP upgraded on the way, with the S/4HANA decision kept open inside the original budget. B #### S/4HANA on AWS, migrate and convert in one step Move to AWS and convert to S/4HANA in a single program with one cutover, one test cycle and one business freeze. Your custom code comes with you. Fits when The organization can absorb one larger change and the customizations are worth keeping Watch for Heavier testing and change management; the wave plan and rehearsal cadence carry the risk The skie.io SAP team has converted a multi-plant packaging manufacturer from ECC to S/4HANA by brownfield in six months, with six hours of technical downtime and 1,000+ custom objects carried forward. SAP runs the SAP box SAP-managed subscription. skie.io lands and operates everything around it on AWS. C #### SAP Cloud ERP Private (RISE with SAP) on AWS Single-tenant, SAP-managed S/4HANA on AWS under a RISE subscription (formerly S/4HANA Cloud, private edition, or PCE). Brownfield conversion is allowed and custom code carries over. Fits when A single SAP contract and reduced infrastructure ownership outweigh flexibility and cost control Watch for The infrastructure sits in SAP's account, not yours; the rest of the estate still needs a home and an operator The skie.io SAP team has moved full SAP estates (ECC, BW, GRC, BusinessObjects) from on-premises Oracle Database into SAP's managed private cloud, halving migration time through repeated test runs. D #### SAP Cloud ERP thin core, cloud-native periphery Finance and materials management land fit-to-standard on SAP's multi-tenant public edition (GROW with SAP, formerly S/4HANA Cloud Public Edition), as a new greenfield implementation. The processes that differentiate you are rebuilt as cloud-native applications on AWS, on a data layer you own. Fits when A smaller estate, one to three company codes, and most of the custom code is around SAP rather than inside it Watch for Strict fit-to-standard in the core; the processes you keep custom move out of SAP, not into it Every path is delivered by senior, certified SAP and AWS consultants. On every path, skie.io leads the assessment, the surrounding estate, the integration layer and day-to-day operations, so the environment lands optimized and stays that way. AWS migration funding applies to the estate landing in your AWS account, not to an SAP-managed tenant. ### Assessment-led. Landed optimized. Operated from day one. Rightsizing, storage tiering, license optimization and consolidation happen before cutover, so the environment starts at its efficient baseline instead of drifting toward it. 1 #### Assess AWS Transform discovery plus SAP readiness and usage analysis. About 30 days to a signed baseline and a costed four-path comparison; a deeper 6-8 week process assessment when a conversion or SAP cloud edition is on the table. 2 #### Plan Wave plan by dependency cluster, target architecture on AWS, and the AWS funding pathway confirmed in writing before anything moves. 3 #### Migrate by wave Each wave lands right-sized, tiered, consolidated and patched. No optimization backlog after go-live. 4 #### Operate The skie.io platform takes over on cutover: 24×7 automation-first operations with continuous optimization, so the baseline holds. Agents do the volume. Senior consultants make the calls. The same automation-first, generative-AI platform that runs our customers' cloud operations runs the migration: discovery and dependency mapping, code and license analysis from real usage, a proprietary TCO engine that produces the recommended AWS configuration and run cost for every path, and the runbooks and tests for every wave. Consultants with 25+ years in SAP and infrastructure decide the path, sign the baseline, sequence the waves and approve every production change. Assessment-backed target 20%+ lower steady-state run cost compared with the agreed VMware baseline Our goal, and based on past results our expectation, is 20%+ lower steady-state run cost against the agreed baseline. The assessment validates it for your estate before anything moves. We share the full results with you, and you decide whether to proceed, with skie.io or anyone else. The report and the baseline are yours either way. Baseline, scope, measurement period and responsibilities for the target are defined in the assessment report and, if you proceed, in the statement of work. ### Four places a VMware estate carries cost that AWS does not have to. #### Hypervisor subscription The Broadcom bundle and per-core minimums disappear. Compute is billed for what runs, not for licensed cores. #### Over-provisioning and one-tier storage VMs are right-sized from observed utilization before the move; cold and warm data leave the SAN for tiered EBS and S3, and backup retention moves to S3 Deep Archive. #### Database and software licensing Oracle Database under SAP ECC can move to SAP ASE or SQL Server; scattered SQL Server consolidates; OS and middleware entitlements are matched to license-included, BYOL or open-source options. #### Operations effort Automation-first operations on the skie.io platform replace hardware, hypervisor and patch-cycle toil with a predictable fee. ### Senior-led, full-estate, choice-aware, continuous. skie.io works with SMB and mid-market companies. Few of them have deep expertise in SAP, infrastructure, cloud and security all at once, and a VMware exit with an SAP decision touches every one of those. What they need is a trusted partner who brings all four of these, with automation doing the routine work. #### Senior-led The people who scope the assessment lead the migration. No hand-off from a sales architect to a delivery bench. #### Full-estate SAP, its databases, integration, identity, custom apps and backup move as one planned cluster. #### Choice-aware Four executable S/4 paths, compared on your numbers. Including SAP's own cloud editions, when they are the right answer. #### Continuous Cutover is the start of the relationship. The skie.io platform operates the environment and keeps optimizing it. ### SAP landscapes we have moved, converted and still run today. Programs on AWS, Azure and SAP’s managed private cloud. Every one is referenceable; ask us for the contact and we will arrange the call. Food production #### A large vertically integrated food producer With the cloud decision still open, AWS was chosen on a written 20%+ commitment against the customer's existing infrastructure and managed-services spend. SAP ECC and 40+ applications then moved off VMware to AWS in eight months with zero data loss, SAP upgraded in the same cutover and the S/4HANA decision kept open. skie.io has operated the estate since, and the account has added a data platform and GenAI agents on AWS. 20%+ committed in writing 40+ applications in 8 months 0 data loss Read the case study Mining #### A multi-country mining group SAP S/4HANA moved off VMware to AWS in four weeks with zero incidents. skie.io has operated the environment since cutover. 4 weeks 0 incidents S/4 HANA Read the case study Packaging #### A multi-plant packaging manufacturer ECC 6.0 to S/4HANA brownfield conversion with New GL migration and 100+ Fiori apps; 1,000+ custom objects carried forward. 6 months to go-live 6 hrs technical downtime Read the case study Ceramics #### A global ceramics manufacturer ECC to S/4HANA 1909 on Azure for two countries; highly customized, integrated with CRM, MDM, DMS, PI and portals. 2 countries On time through COVID-19 Read the case study Footwear #### A global footwear brand ECC, BW, GRC and BusinessObjects moved from Oracle Database on premises to SAP HANA in SAP’s managed private cloud. 160→77 hrs migration time 60-70% faster key reports Read the case study Optical networking #### An optical networking manufacturer BW 7.4 on SQL Server to BW/4HANA by remote conversion, within a full-landscape move to SAP’s managed private cloud. 50% faster key reports 33% lower BW maintenance cost Read the case study Semiconductors #### A semiconductor company in 11 countries 24×7 SAP Basis, functional and infrastructure managed services across 16 data centers, replacing an offshore incumbent. ~15% total IT savings 24×7 coverage, from 5×12 Read the case study ### Senior consultants with 25+ years in SAP and infrastructure, working with agents, not a delivery bench. The consultants who scope your assessment lead your migration and stay accountable through cutover. The skie.io SAP migration team has led SAP migrations and conversions across ECC, S/4HANA and BW/4HANA, to AWS, Azure and SAP's managed private cloud, including brownfield conversions and moves to RISE. Delivery teams across the US, LATAM and Europe, working in English, Spanish and Portuguese. ### The VMware & SAP assessment Read-only discovery, nothing changes in your environment, then the analysis the tools don't do: a signed baseline, an independent SAP license audit and a costed comparison of the four S/4 paths. About 30 days. You decide what happens next. No commitment. The assessment stands on its own whether or not you migrate with skie.io, and it is eligible for AWS assessment funding. Download the assessment brief → - Estate inventory and dependency map Every VM, database and integration around SAP, and what talks to what - Process and custom-code inventory Every customer enhancement and Z-object classified from real usage: replaced by standard S/4HANA, migrated and upgraded as is, rebuilt cloud-native on AWS, or retired as dead. Scopes any conversion before you pay for it - Agreed run-cost baseline Your current VMware, hardware, storage, backup and operations cost, signed off by both sides - Four-path S/4 comparison Run cost, licensing, change effort and timeline side by side - Independent SAP license audit and optimization plan The edition and FUE count you actually need after conversion, from observed usage rather than from the SAP quote; plus database, OS and middleware with a priced replacement option for each - Target, architecture and AWS funding pathway The 20%+ assessment-backed figure with its assumptions, the wave plan, and MAP eligibility confirmed with AWS ### Decide once, for both clocks. Thirty days of assessment is enough to know whether AWS beats your VMware renewal, which S/4 path fits, and what the program costs, and AWS will often fund it. Start before Broadcom sets the timeline for you. --- # AWS Well-Architected Review and Remediation | skie.io URL: https://skie.io/services/well-architected-review/ Description: Transform your AWS workloads from 'it works' to Well-Architected with automated reviews and continuous optimization across all six pillars ## AWS Well-Architected Services ### Build on a Foundation of Excellence Transform your AWS workloads from "it works" to Well-Architected with automated reviews and continuous optimization across all six pillars Most Well-Architected Reviews end with a PDF report that gathers dust. Ours create living architectures that improve automatically, powered by AI and continuous monitoring. Automated Analysis AI-powered assessment Continuous Improvement Architecture evolves Implementation Included We remediate Measurable Outcomes Real metrics ### The skie AWS Well-Architected Difference #### From Static Review to Living Architecture Traditional Well-Architected Reviews are point-in-time assessments. We deliver continuous architectural excellence through our integrated platform that identifies gaps and automatically closes them. #### Traditional Approach - One-time PDF report - Manual implementation - Outdated in weeks - No follow-through #### The skie Way - Living architecture - Automated remediation - Continuous updates - Measurable outcomes ### The Six Pillars, Reimagined #### How skie Transforms Each AWS Well-Architected Pillar 1 #### Cost Optimization Powered by skie Sage ##### Traditional: Manual spreadsheet analysis, outdated by completion ##### The skie Way: - Connected Intelligence sees all cost dependencies - 100% resource analysis, not sampling - Automated implementation of recommendations - Continuous optimization as usage changes 25-50% typical cost reduction 2 #### Sustainability Powered by skie Sage ##### Traditional: Basic carbon footprint estimates ##### The skie Way: - Right-sizing for minimal resource waste - Automated scheduling for non-production - Graviton migration recommendations - Region optimization for renewable energy 35% average carbon reduction 3 #### Reliability Powered by skie Guardian ##### Traditional: Document backup procedures, hope they work ##### The skie Way: - Automated backup implementation and testing - Multi-AZ and multi-region deployment - Self-healing infrastructure with auto-recovery - Chaos engineering and failure testing >99.99% uptime achieved 4 #### Performance Efficiency Powered by skie Guardian + Muse ##### Traditional: Performance testing during incidents ##### The skie Way: - Continuous performance monitoring - AI-powered anomaly detection - Automated scaling and optimization - Predictive capacity planning 60% performance improvement 5 #### Security Powered by skie Guardian ##### Traditional: Quarterly security reviews ##### The skie Way: - 24/7 automated security monitoring - Automated patching and updates - GuardDuty + CrowdStrike integration - Continuous compliance validation Zero security incidents 6 #### Operational Excellence Powered by skie Muse ##### Traditional: Manual runbooks and procedures ##### The skie Way: - AI-generated automation workflows - Self-documenting infrastructure - Automated incident response - Continuous process improvement 70% reduction in manual tasks ### Our AWS Well-Architected Process #### From Assessment to Excellence in 30 Days Week 1 #### Automated Discovery ##### Comprehensive Architecture Analysis - Deploy automated discovery tools - Map all resources and dependencies - Identify architectural patterns - Baseline current state metrics Week 2 #### Intelligent Assessment ##### AI-Powered Gap Analysis - Sage analyzes cost and sustainability - Guardian evaluates reliability and security - Muse identifies automation opportunities - Prioritized findings with risk scores Week 3 #### Implementation Planning ##### Actionable Roadmap Creation - Quick wins for immediate impact - Phased approach for complex changes - Automated remediation scheduling - Stakeholder communication plan Week 4 #### Automated Remediation ##### Platform-Powered Improvements - Sage implements cost optimizations - Guardian deploys reliability improvements - Muse creates automation workflows - Continuous validation and testing Ongoing #### Living Architecture ##### Continuous Excellence - Monthly architecture health scores - Automated best practice updates - Proactive issue prevention - Quarterly executive reviews ### Why AWS Well-Architected Matters #### The Hidden Costs of Architectural Debt #### Without Well-Architected 40% higher costs from inefficient architectures 3x more outages from reliability gaps 60% slower performance from poor design 5x security incident risk from misconfigurations 200% more overhead from manual processes #### With skie AWS Well-Architected Optimized costs from day one Industry-leading reliability Peak performance maintained Security by design Automated operations ### Service Packages #### Choose Your Path to Architectural Excellence #### Foundation Review Perfect for: Single workload optimization - One-time assessment of specific workload - All six pillars analyzed - Prioritized recommendations - 30-day implementation support Starting at $15,000 #### Enterprise Assessment Perfect for: Organization-wide transformation - Comprehensive multi-account review - All workloads assessed - Implementation roadmap - Quarterly follow-up reviews Starting at $50,000 ⭐ Recommended #### Continuous Excellence Perfect for: Ongoing architectural health - Monthly automated assessments - Continuous remediation - Living architecture maintenance - Included with Guardian Advanced/Premium Starting at $5,000/month ### Success Stories #### Real Architectures. Real Results. #### Global E-commerce Platform ##### Challenge: Failed previous Well-Architected Review with 147 high-risk findings ##### Solution: Continuous Excellence program with automated remediation ##### Results: - 100% high-risk findings resolved in 60 days - 42% cost reduction achieved - Better than 99.99% uptime for 18 months - 80% faster deployment cycles #### Financial Services Company ##### Challenge: Struggling with compliance and security requirements ##### Solution: Enterprise Assessment with Guardian Premium ##### Results: - Achieved SOC 2 compliance in 90 days - Zero security incidents post-implementation - 35% performance improvement - $2.4M annual savings --> ### ROI of Architectural Excellence #### Well-Architected Pays for Itself ##### Typical 1,000 VM Environment: ###### Cost Savings Architecture optimization: $100K/month Incident prevention: $50K/month Automation benefits: $30K/month Total: $180K/month saved ###### Investment Continuous Excellence: $10K/month ROI: 18x monthly --> ### Frequently Asked Questions #### How is skie's approach different from other AWS Well-Architected Partners? We assess, then we implement. Our platform automatically remediates findings, while others leave you with a report. Plus, our Continuous Excellence ensures your architecture stays Well-Architected. #### Can you work with existing AWS environments? Absolutely. Our assessment works with any AWS environment, regardless of age or complexity. We often find the most value in mature environments that have grown organically. #### How do you prioritize which findings to address first? Our AI analyzes risk, impact, and effort to create an optimal remediation sequence. We typically start with quick wins that deliver immediate value, then address complex changes systematically. #### What if we've already had an AWS Well-Architected Review? Previous reviews become outdated quickly. We'll validate previous findings, identify what's changed, and most importantly, help you actually implement the recommendations. #### How disruptive is the remediation process? Most improvements are zero-downtime. For changes requiring downtime, we schedule during maintenance windows and use techniques like blue-green deployments to minimize impact. ### Customer results AWS environments skie built Well-Architected and now operates #### JRC Mining group with operations in Peru, Mexico and Canada. SAP S/4HANA moved off VMware to AWS in four weeks, and skie has operated the environment since. Read the JRC case study → #### San Fernando Peru's largest poultry producer. skie migrated its SAP landscape from VMware to AWS and now operates it under Guardian managed services. Read the San Fernando case study → ### Review Once. Transform Continuously. Your architecture should be a competitive advantage, not a liability. Start your journey to AWS Well-Architected excellence today. --- # Terms of Use | skie.io URL: https://skie.io/terms-of-service/ Description: The terms of use governing your access to skie.io's website, cloud cost optimization platform, and related services. ## Terms of Use Last Updated: January 2025 ### Welcome to skie.io These Terms of Use ("Terms") apply to your use of skie.io's website and services. By using our platform, you agree to these Terms. If you don't agree, please don't use our services. ### What We Do skie.io provides AI-powered cloud optimization and automation services: - Sage: Cloud cost optimization - Muse: AI automation for productivity - Guardian: Managed cloud services ### Your Responsibilities When using our services, you agree to: - Provide accurate information - Keep your account secure - Use our services legally and responsibly - Respect our intellectual property - Not attempt unauthorized access - Not sharing credentials - Not interfere with our services - Not use our services for harmful purposes ### Our Intellectual Property Everything on our platform (logos, content, software) belongs to skie.io or our licensors. You can't use our trademarks or copy our content without permission. ### Privacy & Security Your data is protected according to our Privacy Policy. We take security seriously, but you're responsible for keeping your account credentials safe. ### Important Disclaimers - Our services are provided "as is" without warranties - We're not responsible for service interruptions or third-party content - Our liability is limited to the fees you've paid us - You agree to indemnify us for your misuse of our services ### Account Termination We can suspend or terminate accounts that violate these Terms. You can cancel your account anytime according to your service agreement. ### Enterprise Customers If you're using our enterprise services (Sage, Muse, or Guardian), additional terms in your service agreement apply. ### Legal Stuff - Governing Law: Delaware, USA - Disputes: Resolved in Delaware courts - Changes: We may update these Terms with notice - Entire Agreement: These Terms plus any service agreements ### Contact Us Questions? Contact us at: skie.io, LLC 110 East Broward Blvd, Suite 1700 Fort Lauderdale, FL 33301 Email: legal@skie.io By using skie.io, you agree to these Terms of Use.