A global retail enterprise faced escalating multi-cloud costs and limited visibility into its cloud spending. With operations spanning AWS, Azure, and GCP, the company struggled to control wasteful usage and align cloud expenses with business goals. They engaged Xenonify’s platform (xenonify.ai), an AI-driven autonomous FinOps solution deployed on AWS, to tackle these challenges.
Xenonify provides unified cost analytics, intelligent recommendations, and automated remediation across all clouds. Within six months, the retailer achieved a 25% reduction in total cloud spend, eliminated most idle resources, and improved budget forecasting accuracy. The solution enabled real-time financial transparency and freed engineering teams from manual cost management, resulting in significant operational savings and a fast ROI.
Customer: “RetailCo” (Anonymized Global Retailer)
Industry: E-commerce/Retail
Location: Global (Headquarters in North America)
Company Size: ~30,000 employees (Fortune 500)
RetailCo’s digital business relies on cloud infrastructure to support its online storefront, supply chain systems, and analytics. Rapid growth and a multi-cloud footprint (primarily AWS with strategic use of Azure and GCP) led to ballooning cloud costs without clear accountability. The finance department lacked timely, granular insight into which products or teams were driving spend, making budgeting and forecasting difficult. Business leaders set a goal to reduce wasteful cloud spend and reinvest savings into innovation, but existing tools were inadequate.
The company’s previous approach involved manual reporting and basic cloud vendor cost dashboards, which failed to provide a consolidated view or actionable optimizations. Critical business initiatives were at risk as cloud budgets were regularly exceeded, echoing a common trend where managing cloud spend has become the number-one cloud challenge for enterprises. RetailCo also faced compliance pressures to enforce internal cost governance policies and needed to ensure any cost optimizations would not compromise performance during peak retail seasons.
From an IT perspective, the environment consisted of hundreds of AWS accounts and subscriptions on Azure and GCP, resulting in siloed cost data and complexity. The engineering teams struggled with a lack of automation in cost management – for example, identifying idle EC2 instances, orphaned storage, or underutilized databases required significant manual effort. There was no unified tagging standard, making it hard to allocate costs by department or project. Legacy processes and scripts couldn’t scale to the environment’s size, and integrating cost data across clouds was technically challenging.
Moreover, cloud service complexity meant engineers spent considerable time tuning resource configurations for cost efficiency instead of building features (in industry cases, engineers can waste up to 30% of their time on manual cost tweaks).
Scalability and performance requirements further complicated matters – any solution needed to handle millions of usage data points without performance impact on production systems. Security was also paramount: cross-cloud cost data and any automation had to adhere to the company’s strict access controls and compliance standards. RetailCo needed a sophisticated approach that could integrate with all three clouds and intelligently optimize costs without human intervention, something their existing tools and processes could not achieve.
To address these challenges, RetailCo partnered with Xenonify (the provider of Xenonify) to deploy an AI-powered FinOps platform on AWS. The Xenonify solution uses autonomous intelligent agents to continuously analyze cloud usage, costs, and performance across AWS, Azure, and GCP. The architecture was centered on AWS, leveraging it as the control plane and data lake for multi-cloud cost data.
Xenonify ingested detailed billing and usage data from all three clouds and applied machine learning models to detect anomalies, identify idle or underutilized resources, and forecast future spend. The platform provided a unified dashboard and natural-language insights so both engineers and finance users could query cloud spend (“Which projects exceeded budget last month?”) and get instant answers.
Under the hood, multiple AI agents handled specific tasks: one focused on infrastructure optimization (rightsizing instances, scheduling off-hours shutdowns), another on cost governance (ensuring tagging compliance, monitoring budget vs. actuals), and another on business mapping (allocating costs to business units and products for unit economics). The solution was designed following AWS Well-Architected principles for security, reliability, and performance.
Crucially, Xenonify could also take automated actions – for example, it would programmatically turn off idle dev/test servers or auto-apply purchasing commitments – with built-in guardrails and approval workflows. This autonomous FinOps approach gave RetailCo a scalable way to govern costs in real time across their multi-cloud landscape.
Amazon S3 – Central data lake for storing AWS Cost and Usage Reports and aggregated multi-cloud billing data. All detailed cost records from AWS, Azure, and GCP were consolidated into S3 for analysis.
AWS Glue & Amazon Athena – Used to ETL and query the cost data in S3. Glue jobs consolidated raw billing files, and Athena enabled interactive analysis and AI agents to run cost queries (e.g., identifying top cost drivers by tag).
AWS Lambda – Serverless functions acted as FinOps automation agents. Lambda functions were scheduled to perform tasks like checking for idle resources, enforcing tag policies, and even initiating resource termination or rightsizing in response to Xenonify recommendations.
Amazon SageMaker – Powered the machine learning models for anomaly detection and spend forecasting. SageMaker was used to train models on historical spend patterns and deployed endpoints that the Xenonify platform queried to predict future costs and detect outliers.
AWS Cost Explorer API – Provided programmatic access to AWS cost and usage data, which was ingested into the Xenonify analytics engine. This complemented the detailed billing data for near real-time visibility.
Amazon QuickSight – (Visualization) Enabled custom FinOps dashboards and reports for executives. For example, QuickSight was used to present KPIs like cost per order, monthly cloud spend by business unit, and savings achieved from optimizations, in an easy-to-consume format.
(Additional AWS services such as AWS Organizations for account management and AWS Identity and Access Management for secure cross-account role access were also utilized to safely connect the Xenonify platform with all of RetailCo’s cloud accounts.)
The implementation was executed in phases over approximately 12 weeks. In Phase 1, Xenonify and RetailCo’s Cloud Center of Excellence team set up the core Xenonify platform on AWS. This involved provisioning a secure VPC for the FinOps tooling and configuring data ingestion pipelines from AWS (via Cost Explorer and CUR on S3), Azure (via Cost Management exports), and GCP (via BigQuery billing export). The project followed an Agile methodology, delivering incremental value every 2-3 weeks.
Early on, the team focused on establishing visibility: integrating all accounts and clouds into a single view and defining tagging schemas to improve cost allocation. They used AWS IAM roles and Azure/GCP service principals to grant the platform read-only access (and selective write permissions for approved automation actions) across all environments, addressing security requirements from the outset.
In Phase 2, attention turned to optimization and automation. The team configured Xenonify’ AI agents with RetailCo’s business rules – for example, identifying any VM or database with <5% utilization as “idle” to be flagged for shutdown, and setting cost anomaly thresholds (e.g., alert if any service’s spend jumps 10% week-over-week). They conducted tests in non-production accounts first: the platform’s recommendation engine suggested rightsizing over-provisioned AWS EC2 instances and highlighted orphaned cloud storage volumes.
With approval workflows in place, many of these fixes were then automatically executed by Lambda functions (e.g., unused development servers were stopped at 6 PM). Integration with existing systems was also implemented; for instance, the solution was linked to Slack and email for sending cost anomaly alerts to developers, and to the corporate budgeting tool to feed more accurate forecasts.
Throughout implementation, security and compliance were maintained. All cost data remained in RetailCo’s AWS environment (S3) under encryption, and no sensitive customer data was involved – only metadata and cloud usage stats. The AI models and agents were thoroughly tested to ensure any automated action wouldn’t impact critical workloads (e.g., production resources were set to require manual approval for changes).
Major milestones included a pilot rollout within one business unit, which achieved quick win savings (~$100K in the first month by cleaning up idle resources), and then a company-wide deployment by Week 12.
By project end, RetailCo had the Xenonify system fully integrated with their operations: monthly cloud cost reviews were replaced with continuous FinOps monitoring, and engineering teams started to incorporate cost optimization recommendations into their sprint planning (a cultural shift in line with FinOps best practices).
The solution leveraged several AWS and FinOps best practices from design through execution. Architecturally, it adhered to the AWS Well-Architected Framework, especially the Cost Optimization pillar – embedding cost awareness into every component. For example, the team implemented auto-scaling and serverless AWS components (Lambda, Athena queries on demand) to ensure the FinOps platform itself was cost-efficient and scalable.
An innovative aspect of RetailCo’s approach was integrating Generative AI capabilities: stakeholders could ask natural language questions about cloud spend (using a chat interface tied into the Xenonify backend), making financial data accessible to non-technical users in a conversational way. This reduced reliance on specialized analysts and empowered self-service insights.
The project also followed FinOps best practices. It established a single source of truth for cloud costs and enforced tagging standards across AWS, Azure, and GCP – a foundational practice for cost allocation and accountability. The autonomous agents introduced were an innovative twist: rather than static reports, the platform provided actionable intelligence and automated execution, which is a cutting-edge practice in cloud financial management.
The team applied a DevOps mindset (FinOps integrated with DevOps), treating cost optimizations as code – for instance, using infrastructure-as-code templates to codify schedules for turning off resources, and CI/CD pipelines to update FinOps policies. By bridging Finance and Engineering through tooling and transparent data, RetailCo fostered a culture of cost accountability.
This cultural change, supported by always-on AI agents, exemplified how modern enterprises can run cloud operations efficiently. As a result, RetailCo’s FinOps initiative not only met its cost goals but became a showcase of innovation, aligning with industry trends where mature FinOps programs achieve 20–40% cloud cost reductions and significantly faster value delivery.
The Xenonify deployment delivered substantial business results for RetailCo. Within half a year of adoption, the company realized a 25% reduction in overall cloud spending, translating to several million dollars in annual savings. This came largely from eliminating idle and over-provisioned resources – in fact, idle cloud costs were slashed by roughly 70%, freeing budget that was being wasted on resources that provided no business value.
These savings had a direct impact on the bottom line and allowed reallocation of funds to customer-facing innovations (such as new e-commerce features).
RetailCo also achieved far better financial visibility and predictability. Cloud spending, which used to regularly exceed budgets by 15-20%, is now closely aligned with forecasts – monthly spend is kept within 5% of budget targets, and unexpected cost overruns have been virtually eliminated. Finance leaders gained the ability to do precise showback of costs to each business unit, improving accountability and planning.
The budgeting process that once took weeks of reconciling data is now largely automated and continuously updated. This means the company can forecast cloud expenses with ~90-95% accuracy, supporting more agile decision-making.
Another major outcome was operational efficiency. By automating manual cost management tasks, RetailCo’s teams saved significant time – the FinOps platform handles tasks that consumed dozens of analyst hours each month. Engineers and cloud architects report spending less time firefighting cost issues and more time on strategic work.
We estimate that the engineering organization recovered around 20% of the time previously spent on cost management chores, effectively enabling faster feature delivery. Additionally, the FinOps initiative gave RetailCo a competitive advantage: they can confidently invest in cloud expansions (for global scaling and seasonal peaks) knowing that an autonomous system will keep optimization in check.
The calculated return on investment (ROI) for the Xenonify solution was very quick – within ~3 months the savings exceeded the cost of implementation, and the payback period for the project was achieved in the first quarter post-deployment. Overall, the business outcomes included not just cost savings, but increased agility and financial control in the cloud.
In addition to business metrics, RetailCo realized numerous technical benefits from the Xenonify solution. Performance and utilization of the cloud environment improved markedly. By rightsizing instances and cleaning up underused services, the average utilization of compute resources increased (e.g., average CPU utilization on EC2 rose from 15% to 50% on optimized workloads), ensuring hardware was used more efficiently. Importantly, these optimizations did not degrade end-user performance; in some cases, rightsizing actually improved application performance by matching instance types more appropriately to workloads.
The environment also became more scalable and reliable. With autoscaling and scheduling in place, the infrastructure now dynamically adjusts to demand – for example, non-critical systems scale down during low-traffic periods – which not only saves cost but also reduces strain, thereby improving reliability during peak loads (no more over-provisioning “just in case”).
The solution strengthened security and compliance as well. By removing unused resources and enforcing consistent tagging, the attack surface was reduced and cloud resources became easier to track and audit.
The company’s compliance team appreciated that cost governance policies (like requiring encryption or certain instance types for compliance-sensitive data) were baked into the FinOps rules, ensuring that cost optimization efforts did not violate any security guidelines. Additionally, technical debt related to cloud infrastructure was reduced: many legacy resource configurations were updated or retired as part of the optimization, streamlining the environment.
Another key technical benefit was improved development velocity. With AI agents taking over routine cost oversight, developers were less interrupted by cost-related alerts or last-minute budget cut demands. This, coupled with clearer cost insights during design phases, meant teams could make more informed architecture choices earlier (e.g., choosing a serverless or AWS Fargate approach for cost efficiency).
The net effect was faster delivery of applications with cost-effective designs. In summary, the FinOps platform not only cut costs but also resulted in a cleaner, more efficient cloud architecture: one that is well-instrumented for cost, highly automated, and aligned with best practices. RetailCo’s cloud environment is now better optimized and more resilient, demonstrating that cost optimization and technical excellence can go hand-in-hand.
Customer Testimonial
Implementing an autonomous FinOps program at this scale was not without challenges. One significant hurdle was organizational resistance and trust – initially, some engineers were wary of an AI-driven system making changes to their infrastructure. To address this, the project was careful to implement changes gradually and transparently: early optimizations were run in “recommendation mode” only, and wins were demonstrated (such as safely shutting down a set of unused test servers) to build confidence.
Another challenge was data quality and integration. During deployment, the team discovered inconsistent tagging and incomplete cost attribution for some workloads. This was overcome by initiating a tagging campaign and updating internal policies, with Xenonify helping to flag non-compliant resources. The integration of three different cloud platforms also posed difficulties – each provider has different data formats and cost models. The solution team had to build custom data ingestion and normalization logic for Azure and GCP, and adjust the AI models to account for different pricing constructs (e.g., AWS Reserved Instances vs. Azure Hybrid Benefit).
Timeline pressures were present as well – RetailCo needed to start seeing savings in the same fiscal quarter to meet CFO targets. This was managed by prioritizing “low-hanging fruit” optimizations first (like cleaning up idle resources and unused storage, which could be done quickly for immediate impact) before tackling more complex optimizations.
There were also technical adjustments: for example, initial attempts at automated rightsizing in a few cases impacted performance of a legacy application; the team learned from this and implemented additional guardrails (such as never downsizing certain critical databases without human review).
By the end of the project, these challenges had been addressed through a combination of tool refinement, policy updates, and stakeholder education. RetailCo emerged with a stronger FinOps discipline, having overcome the early adoption hurdles.
Throughout this journey, several best practices were identified that can benefit other organizations pursuing FinOps. Executive sponsorship and cross-functional alignment proved crucial – having the CFO and CTO jointly champion the project set the tone that cloud cost is a shared responsibility. Regular communication between finance, engineering, and the Xenonify implementation team helped ensure objectives remained aligned (for instance, engineering knew which cost optimizations were highest priority for finance).
Another best practice was to treat FinOps as an ongoing practice rather than a one-time project. RetailCo set up a FinOps governance board that continues to meet monthly, reviewing key metrics and ensuring the AI recommendations align with business priorities.
Investing in tagging and cost allocation foundations early paid dividends. By standardizing tags across clouds and enforcing them (with help from automation), the company ensured that cost reports and optimization actions could be tied to business context (teams, applications, environments). This greatly increased accountability and also enabled more advanced metrics like cost per transaction to be calculated.
The use of automation and “everything-as-code” was another best practice: coding cost policies (e.g., idle resource shutdown schedules) in infrastructure-as-code templates meant they were version-controlled and repeatable. It also reduced human error and labor in executing routine cost-saving measures. Additionally, incorporating FinOps into the development lifecycle was identified as key to long-term success – for example, now architects at RetailCo must consider cost implications as part of design documents, and CI/CD pipelines include checks for cost-intensive resources.
Finally, the project highlighted the benefit of small, iterative wins: by starting with simple clean-ups and showing results, the team gained momentum and buy-in for tackling more complex optimizations. These practices collectively contributed to a successful FinOps program and can serve as a template for others aiming to balance innovation with cost efficiency.
Following the success of the initial deployment, RetailCo is set to deepen its partnership with Xenonify through a series of forward-looking initiatives. These efforts aim to expand FinOps coverage, strengthen predictive analytics, and extend cost optimization practices beyond the cloud into broader IT spend areas. Together, RetailCo and Xenonify are building a sustainable, data-driven approach to financial operations powered by AI-driven intelligence and automation.
Extend FinOps visibility to additional Azure and GCP workloads as multi-cloud adoption grows.
Integrate SaaS cost management for related services to create a complete financial view.
Leverage unit economics modeling to connect cloud spend with per-customer or per-order costs, enabling data-backed profitability insights and pricing decisions.
On the AWS side, include native tools like AWS Compute Optimiser and AWS Budgets to enhance AI agent recommendations and automate budget enforcement.
Utilize collected FinOps data for scenario modeling, such as predicting spend impact if traffic or usage doubles.
Enable proactive reservation and commitment planning based on forecasted trends.
Align with Xenonify’s roadmap for next-gen AI models that continuously learn from usage patterns and evolving cloud architectures.
Explore applying FinOps methodologies beyond cloud infrastructure — to on-prem systems, software licensing, and other IT spend categories.
Reuse learnings from cloud optimization to establish a unified financial governance framework across the enterprise.