Journal of Advances in Developmental Research

E-ISSN: 0976-4844   •   Impact Factor: 9.71

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AI-Driven FinOps for Multi-Cloud Cost Optimization

Author(s) Shailaja Beeram
Country United States
Abstract As cloud adoption accelerates, organizations increasingly face challenges in managing and optimizing operational costs across multiple providers such as Microsoft Azure, AWS, and Google Cloud. Traditional FinOps (Financial Operations) practices rely heavily on manual analysis and static thresholds, often leading to inefficiencies and reactive decision-making. This paper presents an AI-driven FinOps model that integrates predictive analytics, automation, and intelligent workload optimization to manage costs across heterogeneous cloud environments. Leveraging tools such as Azure Cost Management, Machine Learning, and cross-cloud APIs, the proposed architecture enables real-time visibility, anomaly detection, and automated budget governance. Experimental analysis demonstrates that AI-driven FinOps reduces cloud cost variance and improves forecasting accuracy, providing a foundation for sustainable, data-driven financial governance.
Keywords FinOps, multi-cloud cost optimization, AI-driven analytics, Azure Cost Management, automation, predictive budgeting, anomaly detection, workload scheduling, machine learning, Azure Automation, governance, AWS Cost Explorer.
Field Engineering
Published In Volume 17, Issue 1, January-June 2026
Published On 2026-03-26
Cite This AI-Driven FinOps for Multi-Cloud Cost Optimization - Shailaja Beeram - IJAIDR Volume 17, Issue 1, January-June 2026. DOI 10.71097/IJAIDR.v17.i1.1779
DOI https://doi.org/10.71097/IJAIDR.v17.i1.1779
Short DOI https://doi.org/hbtv4k
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