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Success Story

Turning Cloud Cost Chaos into Predictable, Actionable Savings

A fast-growing SaaS company operating across AWS and Azure struggled with fragmented cost visibility, slow anomaly detection, and low engineering engagement in cost optimization. By implementing a FOCUS-standardized cost lake combined with agentic automation and workflow-driven actions, the organization gained faster insights, predictable budgets, and sustained cost control.

  • cost reduction achieved
    within 90 days

    15-30%

  • Anomalies detected
    faster, shifting from
    days to minutes

    100x faster

  • Reliable, predictable
    budgets enabled by
    a unified, FOCUS-standardized cost lake

About the client

The client is a North America–based SaaS company running production workloads across AWS and Azure. Their environment includes Kubernetes-based platforms and rapidly scaling data services, requiring strong cost governance without slowing engineering velocity.

Challenges

The organization faced inconsistent tagging and cost allocation across cloud environments, making accurate chargeback difficult. Cost anomalies were often detected too late, commitments were underutilized, and engineering teams had limited visibility or motivation to act on cost optimization recommendations.

Solutions

FOCUS-Standardized Cost Lake:

Centralized and normalized cloud cost data for consistent allocation and reporting.

Agentic Anomaly Detection:

Identified unusual spend patterns in minutes instead of days.

Automated Commitment Modeling:

Improved coverage and utilization of cloud commitments.

Kubernetes Cost Allocation:

Delivered clearer visibility into workload-level spend.

Workflow Automation:

Integrated Jira and Slack to drive cost actions directly into existing engineering workflows.

Executive Dashboards:

Provided leadership with clear, unified views of cost performance and budget variance.

Results

The company achieved sustained cost savings while improving budget predictability and reducing variance. Faster anomaly detection minimized financial risk, and explainable, low-friction workflows increased engineering participation in cost optimization efforts, embedding cost awareness directly into day-to-day operations.

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