Multi-Agent Generative AI Framework for Cloud Cost Prediction and Intelligent Resource Optimization

Abstract

Therapidadoptionofcloudcomputingservicessuch as Amazon Web Services, Microsoft Azure, and Google Cloud Platformhastransformedenterpriseinfrastructuremanagement by enabling elastic resource provisioning. However, dynamic re- source allocation mechanisms and complex pricing models often lead to unpredictable cloud expenditures and budget overruns. Conventionalcloudmonitoringdashboardsprimarilyprovidede- scriptiveanalyticsandlack predictiveinsights, anomalydetection, and automated optimization capabilities. This paper presents a Multi-Agent Generative Artificial In- telligence framework for proactive cloud cost prediction and intelligent resource optimization. The proposed system consistsof five cooperative agents, including a Data Ingestion Agent,Cost Prediction Agent, Anomaly Detection Agent, Optimization Recommendation Agent, and an LLM-Based Report Genera-tion Agent. A Long Short-Term Memory (LSTM) network is employed for multivariate time-series cost forecasting, while an Isolation Forest model is utilized to identify abnormal billing patterns. A risk-aware optimization mechanism translates an- alytical insights into actionable cost-saving recommendations, and a Generative AI-based Large Language Model produces explainable financial reports for decision-makers. Additionally, ensemble learning models including Random Forest and XGBoost are integrated using a stacking-based meta model to enhance prediction robustness. Experimental results indicate high forecasting accuracy based onthecoefficientofdetermination,robustanomalydetectionwith minimal false alerts, and efficient end-to-end system response, validating the framework’s scalability, interpretability, and suit- ability for production deployment.

Introduction

The text explains that while cloud computing offers flexibility and cost efficiency through pay-as-you-go models, managing cloud expenses has become challenging due to unpredictable costs, over-provisioning, poor configurations, and lack of real-time insights. Traditional tools mainly show past spending and rely on manual analysis, making it difficult to prevent cost issues in advance.

To address this, the study proposes a multi-agent AI-based system that provides predictive, diagnostic, and optimization insights for cloud cost management. The system integrates multiple components: data ingestion, cost prediction using models like LSTM, anomaly detection using Isolation Forest, optimization recommendations, and AI-generated financial reports.

The methodology involves processing cloud billing and usage data, forecasting future costs through time-series analysis, detecting unusual spending patterns, and generating actionable recommendations to reduce costs. The system also produces easy-to-understand reports using generative AI.

Results show that the LSTM model achieves high prediction accuracy (R² ≈ 0.96), outperforming traditional models. The anomaly detection system is highly effective with high precision and recall, and low false positives. The overall system is efficient, with fast response times (around 3.9 seconds), making it suitable for real-time monitoring.

Conclusion

ThispaperpresentedaMulti-AgentGenerativeArtificialIn- telligence framework for intelligent cloud cost prediction and resource optimization. The proposed system integrates pre- dictive modeling, anomaly detection, risk-aware optimization, and automated report generation into a unified architecture. Unliketraditionalclouddashboardsthatprovideonlyhistorical insights, theproposedframework enablesproactivefinancial decision-makingusingpredictiveanalyticsandintelligentrec- ommendations. TheLSTM-basedmultivariatetime-seriesmodeleffectively capturescomplexcostpatterns,workloadvariations,andlong- termbilling dependencies. TheIsolationForest-basedanomaly detection module successfully identifies abnormal cost behav- iorswithminimalfalsealerts.Furthermore,therisk-awareop- timization mechanism converts analytical insights into action- able cost-saving strategies. The LLM-based reporting agent improves interpretability by transforming analytical outputs into structured and human-readable financial summaries. Experimental evaluation demonstrates strong performance, achieving a high prediction accuracy with an R² score of 0.96 and a low false positive rate of 3.7

References

[1] G. N. Junior and C. Marcon, “OPTIC – Optimized Translation andInteraction for Cloud-Costs: Use of FOCUS Standardization and LLMIntegration for Cloud Billing Analysis,” IEEE International Conferenceon Cloud Computing, 2025. [2] S. Santhosh, “Cloud-Based Software Development Lifecycle,” Interna-tional Journal of Software Engineering and Applications, June 2023. [3] Y. Zhang, T. Zhang, and Q. Li, “Cloud Resource Cost Prediction UsingDeep Learning Approaches,” IEEE Transactions on Cloud Computing,2022. [4] A.Choudhary,“ComparativeStudyofVariousCloudServiceProviders,” InternationalJournalofComputerApplications,2022. [5] N.Bhatte,“ComparisonofDifferentCloudProviders,”InternationalJournal of Advanced Research in Computer Science, June 2022. [6] A. M. Mohamed, “Multicriteria Optimization Model for Cloud ProviderSelection,” Journal of Cloud Computing, May 2019. [7] F.T.Liu,K.M.Ting,andZ.H.Zhou,“IsolationForest,”IEEEInternational Conference on Data Mining, 2008.

Copyright

Copyright © 2026 Vinothkumar G, Narthana Hari S V, Samyucthan M, Angela Lincy N H. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.