AN AI DRIVEN MULTI CLOUD FINOPS FRAMEWORK FOR REAL- TIME COST GOVERNANCE AND ANAMOLY DETECTION

Abstract

The purpose of the study was to introduce and test an AI-based multi-cloud FinOps system that could allow managing costs in real-time and measuring anomalies in heterogeneous cloud-based settings. With the growth in the adoption of multi-clouds, organizations were faced with issues concerning financial visibility, cost optimization, and the ability to detect abnormal spending patterns in a timely manner. To combat these challenges, the study combined machine learning, automated governance policies, and single intelligent cost analytics into one system. The study utilized a mixed-method approach by analysing historical data on cloud billing, expert interviews, and experimental simulations in the AWS, Azure, and Google Cloud settings. The findings revealed very high accuracy in anomaly detection, low reach-to-detect latency and substantial decrease in unnecessary cloud spending. The framework was also proven effective by expert feedback to enhance cost visibility, compliance, and operational decision-making. Generally, the research paper has shown that AI-assisted FinOps systems were a scalable and efficient tool to handle the financial complexities in multi-cloud environments that led to predictive and autonomous cloud financial operations.