Optimizing AWS Costs With Machine LearningDriven Recommendations

Abstract

This article presents a novel approach to AWS cost optimization through machine learning-driven recommendations. Cloud spending inefficiencies cost organizations billions annually, with many businesses overprovisioning resources by 25-40%. The proposed solution leverages machine learning algorithms to analyze resource utilization patterns, detect anomalies, predict future requirements, and provide automated recommendations for optimizing AWS infrastructure costs. The implementation framework integrates with DevOps workflows, offering adaptive resource scheduling, intelligent instance selection, and dynamic scaling policies. A real-world implementation at a financial services company demonstrated a 34% reduction in cloud spending within three months while maintaining performance and reliability. The approach provides continuous improvement through reinforcement learning mechanisms, allowing organizations to achieve sustainable cost optimization while preserving operational excellence.