AI-Driven Models for Predictive Cost Optimization in Multi-Tenant Cloud Environments
Balkrishna Patil, Satya Prakash, Anil Kumar Jonnalagadda · 2025
In today's cloud-driven world, being in control of one's costs is more than a financial imperativethe new gateway to sustainable innovation. This paper focuses on exploring AI-driven predictive cost optimization models for a multi-tenant cloud environment, based on an industrial dataset. Our solution combines state-of-the-art machine learning models with deep domain expertise to accurately forecast cost trends and resource consumption patterns. These models dynamically adapt to the ever-changing multi-tenant infrastructure demands by analyzing various usage scenarios, hence providing actionable insights that help in reducing unnecessary spending and improving efficiency. The real-world dataset makes sure our results are relevant to practical realities and bridge the gap between theoretical research and everyday business challenges. This work reveals both significant cost savings and serves as the cornerstone for a far more resilient and responsive cloud management practice. Our findings, at last, draw attention to how AI holds an immense promise of redefining cost optimization methods, thereby facilitating more affordable, approachable cloud environments for businesses of all kinds.