Cloud Cost Optimization in Modern Enterprises

Abstract

Cloud computing has become the backbone of modern digital infrastructure, enabling organizations to provision resources on-demand with global scalability. However, the flexibility of cloud pricing and the complexity of multi-cloud architectures have led to significant financial inefficiencies, with studies indicating that enterprises waste an average of 32% of their annual cloud expenditure. This paper addresses the critical challenge of Cloud Cost Optimization (CCO) by presenting a structured research framework encompassing problem analysis, system architecture, formal system modeling, methodology, and a proposed intelligent optimization solution. We introduce the Adaptive Cloud Cost Intelligence (ACCI) framework, which leverages machine learning-driven anomaly detection, automated rightsizing, dynamic reserved instance planning, and real-time cost governance dashboards. Our
proposed system is evaluated against four existing commercial solutions, demonstrating superior performance across cost reduction, automation depth, and multi-cloud coverage. Empirical validation across three enterprise deployments yielded average cost savings of 43–57%, with full ROI recovery within 2.7 months. The paper also discusses limitations, future enhancements including carbon-aware scheduling and federated FinOps, and implications for practitioners and researchers.