Multi Cloud Environments: ML Enhanced Cost Optimization

Varying pricing policies and dynamic consumption patterns across platforms such AWS, Azure, and

GCP make multi-cloud deployments difficult to manage cost-wise. This research offers a machine learning (ML)enhanced method to Cloud Financial Management (FinOps) giving real-time cost reduction recommendations by means

of resource use patterns analysis across several clouds. Emphasizing the drawbacks of manual and rule-based

approaches, we address the issue of multi-cloud cost control and evaluate relevant cloud cost optimization studies. Our

suggested solution architecture then shows predictive resource allocation, grouping of usage patterns, anomaly

detection, and cost forecasting using supervised and unsupervised ML models. Covered are the methodology and

implementation specifics including data integration from cloud-native cost API, AWS Cost Explorer, Azure Cost

Management, GCP Billing Export into a consistent repository and the training and deployment of ML models for

actionable insights. The ML-driven strategy shows notable decrease in wasted expenditure and enhanced cost

efficiency in a proof-of-concept assessment. By rightsizing resources and taking use of price differences in multi-cloud

configurations, we show how ML may lower operational expenses. The presentation ends with a conversation on the

advantages, difficulties such as model interpretability and data governance and future perspectives for ML-driven cost

optimization in corporate multi-cloud setting