Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Volume 7 Issue 1
January-February 2026

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An AI-Driven Framework for Predictive and Adaptive Cloud Cost Management

Author(s) Anthony Xavier, Vinaya Bhoi, Jayesh Shinde
Country India
Abstract Cloud computing offers flexibility and scalability but also poses challenges in managing resource efficiency and costs. This study investigates AI-driven cloud cost optimization by combining a literature survey with recent empirical findings. Using predictive analytics, reinforcement learning (RL), and optimization algorithms, we examine how AI techniques forecast demand, automate scaling, and adjust workloads to minimize expenses. Our methodology includes simulating cloud workloads and applying AI models to allocate resources dynamically, with performance evaluated on key metrics. Results show substantial cost savings: AI forecasting (e.g. LSTM vs ARIMA) reduced provisioning errors, cutting over-provisioning waste by ~23%; RL-based autoscaling (DQN/PPO) improved utilization by 30% and saved 27% of cloud spend; multi-cloud workload placement with genetic algorithms achieved 19% cost reduction. Anomaly detection models (variational autoencoders, isolation forests) achieved 91% precision in flagging billing spikes, reducing false positives by 40% relative to rule-based methods. These findings indicate AI can significantly reduce costs without compromising performance. We also observe environmental benefits: optimized AI-driven scaling cut carbon emissions by an estimated 67.5% through smarter workload distribution. The paper discusses implications for cloud economics and FinOps, highlights challenges (data privacy, model complexity), and suggests future research on edge-cloud integration and explainable AI. Overall, AI-driven strategies are shown to enhance efficiency, sustainability, and financial control in cloud environments.
Published In Volume 7, Issue 1, January-February 2026
Published On 2026-01-07
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E-ISSN 3048-7641
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