FinOps at Scale: Reducing National Cloud Waste Through Predictive Optimization and Multi Cloud Governance

Abstract

This paper will investigate the possibilities of predictive optimization and multi-cloud FinOps governance to decrease the cloud waste on the national scale. Based on the 2,000 workloads in the AWS, Azure, GCP, Oracle Cloud, and on-premises Kubernetes, the study analyses cost-efficiency, resource usage, auto scalability, and maturity in governance. The predictive model is useful in the improvement of demand forecasting, improved rightsizing, and scaling decisions, and governance in improving tagging compliance and budget control and detection of anomalies. The hybrid strategy causes significant improvements in cost avoidance and uses in all platforms. The findings demonstrate that predictive FinOps is a viable and practical tool that can be utilized to manage big cloud expenditures and enhance business sustainability.