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

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.

Authors

Publication Details

Journal
World Journal of Advanced Engineering Technology and Sciences
Published
2025-05-06
DOI
https://doi.org/10.30574/wjaets.2025.15.2.0290
Primary Topic
Cloud Computing and Resource Management
Type
article
Field-Weighted Citation Impact
0.00
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article

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

S. Sampath
World Journal of Advanced Engineering Technology and Sciences
Cloud Computing and Resource Management
article

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

S. Sampath
article en

Abstract

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.

World Journal of Advanced Engineering Technology and SciencesVol. 15(2)
Openalex Percentile: Top 87%
Cloud Computing and Resource Management
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