AI-Driven Strategies for Cloud Cost Optimization

Author(s)	Anup Raja Sarabu
Country	United States
Abstract	Cloud infrastructure has become essential for modern enterprises, but managing associated costs presents significant challenges. Organizations struggle with overprovisioning, resource inefficiencies, and unexpected billing spikes, wasting substantial portions of their cloud spend. Artificial intelligence offers powerful solutions by introducing data-driven decision-making into cloud resource management. This article explores five AI-powered strategies for cloud cost optimization: machine learning in predictive cost management, AI-optimized resource allocation and workload auto-scaling, comparative solutions from major providers like Google Cloud's Recommender AI and AWS Compute Optimizer, serverless computing paired with AI, and approaches to overcome challenges in implementation. Organizations implementing these technologies achieve substantial cost reductions while maintaining or improving application performance, demonstrating that AI-driven optimization represents the future of efficient cloud financial management.
Keywords	Cloud optimization, artificial intelligence, predictive analytics, serverless computing, resource allocation
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 16, Issue 2, April-June 2025
Published On	2025-05-10
DOI	https://doi.org/10.71097/IJSAT.v16.i2.4714
Short DOI	https://doi.org/g9kc7n

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