Automated Right-Sizing of Cloud Compute Resources: A Data-Driven Framework for Enterprise Cost Optimization

Abstract

Cloud infrastructure environments frequently suffer from resource over-provisioning, leading to significant financial inefficiencies across enterprise organizations. This article introduces an integrated FinOps-aware optimization framework that combines continuous monitoring, intelligent workload analysis, and automated remediation within a unified platform engineering architecture. The solution leverages real-time utilization metrics across CPU, memory, and disk I/O parameters through multiple observability platforms. An analytics engine categorizes workloads into distinct usage patterns—burstable, steady, and idle—enabling precise resource optimization recommendations. The framework integrates with infrastructure-as-code tools to execute automated remediation pipelines, adjusting instance types, container resource limits, and node configurations based on actual demand patterns. Implementation follows blue-green deployment strategies ensuring zero-downtime transitions during resource adjustments. Results demonstrate substantial cost reduction potential while maintaining performance standards: 35-45% cost reduction per optimized instance, CPU utilization improvement from 15-20% to 65-75%, and memory allocation efficiency increased from 30% to 70-80%. This data-driven solution represents a significant advancement in FinOps practices, offering a scalable model for organizations seeking to optimize cloud expenditure without compromising service quality.