AI-driven multi-cloud cost allocation: Transforming FinOps through automation

Sampath, Sridhar (2025) AI-driven multi-cloud cost allocation: Transforming FinOps through automation. World Journal of Advanced Engineering Technology and Sciences, 15 (2). pp. 203-210. ISSN 2582-8266

The adoption of multi-cloud strategies has introduced significant complexity in managing and allocating cloud costs across several cloud platforms. Traditional cost allocation methods, heavily dependent on manual processes, face challenges in providing timely insights and accurate attribution. Artificial Intelligence (AI) and Machine Learning (ML) are transforming this landscape by automating resource tagging, enabling real-time cost attribution, and providing predictive analytics capabilities. Through pattern recognition and automated response mechanisms, these technologies enhance cost visibility, optimize resource utilization, and improve financial governance across cloud environments. The implementation of AI-driven solutions demonstrates substantial improvements in cost attribution accuracy, reduction in manual efforts, and enhanced ability to forecast and optimize cloud spending patterns across different business units and projects.