Engineering-Driven Cloud Cost Optimization at Enterprise Scale: An Applied Success Story with Measured Outcomes in a Large Healthcare Enterprise

Abstract

 Enterprises continue to experience rising cloud infrastructure costs as application portfolios expand and cloud‑native architectures proliferate. This paper presents a production‑validated, engineering‑driven cloud cost optimization framework implemented at a large U.S. healthcare enterprise operating a multi‑account, multi‑region platform with unpredictable demand, strict compliance requirements, and high‑availability expectations. The framework integrates utilization‑based rightsizing, demand‑aware autoscaling, storage lifecycle management, commitment‑based pricing, and continuous governance through policy‑as‑code, explicit cost ownership, and cost–performance observability, embedding cost optimization into routine operations via automated enforcement and recurring review checkpoints. Over two fiscal years, the initiative reduced annualized infrastructure cost by approximately 18% in year one and an additional 5% in year two, despite continued growth in platform demand and overall spend, while availability, latency, and error rates remained within established service‑level objectives. The results demonstrate that cloud cost efficiency can be operationalized as a continuous engineering discipline – complementing site reliability engineering practices – rather than treated as an episodic financial exercise, and provide a repeatable, scalable model for enterprises seeking measurable and sustainable optimization.