The Challenge
Multiple enterprises across Cloudrho’s client portfolio shared a common problem: AWS bills that grew unpredictably month-to-month, engineering teams that lacked the time and tooling to act on cost recommendations, and a procurement model that locked them into On-Demand pricing despite predictable baseline workloads. One-time optimization projects delivered savings that eroded within two quarters as new services were deployed without cost guardrails.
Our Approach
Cloudrho developed a repeatable Autonomous Cloud Cost Optimization engagement model delivered in partnership with two leading FinOps platforms. The model combined intelligent commitment purchasing — using ML-driven forecasting to purchase Reserved Instances and Savings Plans at optimal coverage levels — with continuous rightsizing that flags and auto-remediates oversized resources without engineering intervention.
Onboarding was completed in under two weeks per client, requiring read-only AWS Cost and Usage Report access and a 30-minute kickoff. Cloudrho’s FinOps engineers performed the initial baselining, set commitment targets, and configured anomaly detection thresholds aligned to each client’s risk tolerance.
A monthly governance cadence — the “FinOps Business Review” — kept finance and engineering stakeholders aligned on savings realized, commitments expiring, and upcoming architecture changes that required coverage adjustments. Savings were reported on a net basis after platform fees, ensuring full transparency of ROI.
Business Outcomes
Unlike project-based optimizations that degraded over time, the autonomous model delivered compounding savings as commitment coverage and rightsizing recommendations adapted continuously to workload patterns — effectively creating a self-funding cloud cost discipline embedded in operations.