Improving Enterprise Cloud Utilization through FinOps-Based Cost Analytics and Adaptive Resource Management
DOI:
https://doi.org/10.15680/IJMRSETM.2026.0210002Keywords:
FinOps, enterprise cloud computing, cloud cost analytics, resource optimization, adaptive resource management, cloud utilization, cost efficiency, workload forecasting, automated scaling, financial accountabilityAbstract
Enterprise cloud computing has become a fundamental component of digital transformation, enabling organizations to scale applications, accelerate innovation, and improve operational flexibility. However, increasing cloud adoption has also introduced challenges related to resource underutilization, unpredictable expenditure, inefficient provisioning, and limited cost visibility. This study examines how FinOps-based cost analytics and adaptive resource management can improve enterprise cloud utilization while maintaining performance, reliability, and operational efficiency. The research adopts a structured methodological approach that integrates cloud cost analysis, resource utilization monitoring, financial accountability, and adaptive optimization strategies. FinOps principles are applied to establish collaboration among finance, engineering, and operations teams, enabling informed decisions regarding cloud consumption and expenditure. Cost analytics techniques, including usage forecasting, anomaly detection, and cost allocation, support the identification of wasteful spending and opportunities for optimization. Adaptive resource management mechanisms, such as automated scaling, rightsizing, workload scheduling, and resource provisioning adjustments, are examined as complementary approaches to improving utilization. The proposed framework evaluates cloud efficiency through indicators such as cost per workload, resource utilization rate, budget variance, and service performance. The study is expected to demonstrate how integrating financial intelligence with automated resource management can strengthen cost transparency, improve resource allocation, and support sustainable cloud operations. The findings may provide practical guidance for enterprises seeking to balance financial discipline, technical performance, and scalability in dynamic cloud environments
References
1. Sazu, A. H., Khan, M. A. N., Sultana, S., Tasnim, M., Jakir, T., Alam, A., … Himeluzzaman, M. (2025). Artificial intelligence-based anomaly detection for environmental and financial risk management in banking. International Journal of Advances in Signal and Image Sciences, 2083–2095.
2. Ramasamy, M. (2022). Architecting scalable intent-based networking platforms for enterprise automation. International Journal of Emerging Trends in Engineering and Management Research (IJETEMR), 7(3), 11812–11823.
3. Pochincharla, S. (2025). Secure Explainable Machine Learning Architecture for Smart Enterprise Systems and Real Time Risk Governance. International Journal of Computer Technology and Electronics Communication, 8(5), 11554-11562.
4. Gullapalli, S., Divi, V. R., Muppidi, N. K. R., Rachamalla, S., & Tatavarthi, S. (2026, July). GuardGRPC: Priority-Aware Resource Governance for OTLP/gRPC Telemetry Export. In 2026 5th International Conference on Electronics Representation and Algorithm (ICERA) (pp. 272–277). IEEE.
5. Neela, S. (2024). Secure and zero-trust middleware architectures for real-time enterprise data exchange. International Journal of AI, BigData, Computational and Management Studies, 5(4), 220–227. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I4P124
6. Kasarapu, B. C., Balerao, R., Kammari, P., & Chalicham, H. (2026, August). Design and Implementation of an AI-Driven Conversational Agent for Real-Time HR Support in Retail Environments. In 2026 10th International Conference on Inventive Systems and Control (ICISC) (pp. 1–8). IEEE.
7. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273–287.
8. Madabathula, L. (2026, May). A Resilient Multi-Petabyte Lakehouse Architecture for Regulated Enterprises: Design Patterns and Failure Recovery. In 2026 2nd International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI) (pp. 731–735). IEEE.
9. Tokas, D. (2026). Prioritize workflow mapping before any tech integration in cloud platform engineering. Journal of Computational Analysis and Applications, 35(3), 545–556. https://doi.org/10.48047/jocaaa.2026.35.03.41
10. Sahu, S. K. (2024). Enterprise AI Governance Architecture for Salesforce-Based Healthcare CRM Platforms: A Pattern-Oriented Framework for Regulated AI. American International Journal of Computer Science and Technology, 6(5), 83–88.
11. Muppidi, N. K. R. (2026, July). Secure Context Cache: Token-Efficient and Least-Privilege Shared Memory for Enterprise Developer Agents. In 2026 5th International Conference on Electronics Representation and Algorithm (ICERA) (pp. 679–684). IEEE.
12. Chandran, A. S. (2024). Engineering billion-query search systems: Lessons from large-scale metadata platforms. International Journal of Research and Applied Innovations, 7(2), 10510–10519.
13. Bitragunta, S. L. V. (2025). Optimizing hybrid energy storage in EVs using AI enhanced fuzzy logic control systems. International Journal of Science, Research and Technology, 8(1), 13525–13533.
14. Ahuja, D. (2026, May). A Unified DevOps Framework for Multi-Cloud Orchestration: Automating Cross-Platform Deployments Across Azure, Salesforce, and MuleSoft. In 2026 2nd International Conference on Artificial Intelligence Systems (AIS) (pp. 153–158). IEEE.
15. Bandari, U., & Jinna, K. R. (2024). LLM-assisted anonymization of customer experience data: A comparative study of rule-based, NER, and generative approaches. International Journal of Research and Applied Innovations, 7(1), 10164–10174.
16. Gunasekaran, R. M. N. (2026). AI-Driven Data Governance: Ensuring Compliance in Big Data Ecosystems. International Journal of AI, BigData, Computational and Management Studies, 262–275.
17. Aaviti, P. K. (2026). SRE-Driven Reliability Framework for Enterprise Data Platforms. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 9(4), 1559–1569.
18. Sasikumar, B., Venkatasalam, K., & Rajendran, P. (2024). Induction motor fault detection and classification using rcnn and surf based machine learning algorithms and infrared thermography. Scientia Iranica.
19. Sunkara, R., & Garg, B. (2026). Agentic architectures and autonomous decision-making. Agentic Intelligence and the Future of Autonomous Digital Ecosystems, 1(1).
20. Reddy, K. V. (2025). Automating UVM frameworks using artificial intelligence and machine learning for complex SoC verification. International Journal of Research and Applied Innovations, 8(6), 13283–13287.
21. Vimal Raja, G. (2022). Leveraging machine learning for real-time short-term snowfall forecasting using multisource atmospheric and terrain data integration. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 5(8), 1336–1339.
22. Chinthamani, R. (2023). Engineering resilient microservices for real-time retail order and inventory synchronization. International Journal of Engineering & Extended Technologies Research, 5(6), 7774–7779.
23. Kari, M., & Chandrashekar, P. (2026, March). A Predictive Machine Learning Approach for Enhancing Software Testing Efficiency with Automated Defect Prediction. In 2026 World Conference on Computational Science and Technology (WcCST) (pp. 592–597). IEEE.
24. Mysula, S. (2026). AI-enabled autonomous enterprise integration: Intelligent orchestration of event-driven business workflows. International Journal of Research Publications in Engineering, Technology and Management, 9(3), 1145–1150.
25. Aragani, V. M., Maroju, P. K., & Mudunuri, L. N. R. (2024, December). Implementing Non-functional Production Regression Testing with Kubernetes. In International Conference on Information and Communication Technology for Competitive Strategies (pp. 231–244). Singapore: Springer Nature Singapore.
26. Sunkara, R., & Garg, B. (2026). Agentic architectures and autonomous decision-making. Agentic Intelligence and the Future of Autonomous Digital Ecosystems, 1(1).
27. Basavala, S. R. (2025). Continuous application security posture management through AI-driven risk correlation and vulnerability prioritization. International Journal of Research Publications in Engineering, Technology and Management, 8(3), 12175–12187.
28. Jinna, K. R., & Bandari, U. (2025). Agentic pipeline operations: LLM agents for CI/CD failure triage and remediation under human-in-the-loop guardrails. International Journal of Research and Applied Innovations, 8(3), 13134–13144.
29. Uddandarao, D. P., Mahajan, A., & Krishnamsetty, P. (2024). Double machine learning for downstream causal measurement: A hybrid econometric-ML framework for feature valuation. International Journal of Computational and Experimental Science and Engineering, 10(4), 4477–4485.
30. Kalabhavi, V. (2024). Retail promotion and barcode redemption analytics using AI for marketing budget optimization. International Journal of Future Innovative Science and Technology, 7(4), 13212–13222.
31. Srikanth, V., Kavitha, P. M., Kodamanchili, R., Thatipudi, J. G., Kalpana, R., & Upadhyay, N. (2026, February). An Advanced Internet of Things Enabled Security System Design for Residential Intrusion Detection. In 2026 5th International Conference on Sentiment Analysis and Deep Learning (ICSADL) (pp. 1–6). IEEE.
32. Karrothu, A. (2025). Engineering scalable cloud-native distributed systems for real-time security telemetry ingestion and threat detection. Journal of Computational Analysis and Applications, 34(12), 1175–1188.
33. Bitragunta, S. L. V. (2025). Optimizing hybrid energy storage in EVs using AI enhanced fuzzy logic control systems. International Journal of Science, Research and Technology, 8(1), 13525–13533.
34. Balla, R. T., Padmanabham, S., Bhandari, R. S., Kumar, V., & Singh, G. (2026, February). AI-Driven Optimization of Enterprise Resource Planning Systems: An SEM-Based Analysis of Decision-Making Effectiveness. In 2026 5th International Conference on Innovative Practices in Technology and Management (ICIPTM) (pp. 1–6). IEEE.
35. Aaviti, P. K. (2026). SRE-Driven Reliability Framework for Enterprise Data Platforms. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 9(4), 1559-1569.
36. Kopparthi, G. S. (2025). AI-driven healthcare solutions: Transforming the future of medicine. International Journal of Applied Mathematics, 38(10s), 3308–3317.
37. Gadige, C. D. (2023). Scalable CRM automation for high-volume customer onboarding: An event-driven Salesforce architecture. International Journal of Emerging Trends in Engineering and Management Research, 8(6), 14762–14772.
38. Ramasamy, M. (2022). Architecting scalable intent-based networking platforms for enterprise automation. International Journal of Emerging Trends in Engineering and Management Research (IJETEMR), 7(3), 11812–11823.
39. Sahu, S. K. (2024). Enterprise AI Governance Architecture for Salesforce-Based Healthcare CRM Platforms: A Pattern-Oriented Framework for Regulated AI. American International Journal of Computer Science and Technology, 6(5), 83–88.
40. Reddy, K. V. (2025). Automating UVM frameworks using artificial intelligence and machine learning for complex SoC verification. International Journal of Research and Applied Innovations, 8(6), 13283–13287.
41. Chinthamani, R. (2023). Engineering resilient microservices for real-time retail order and inventory synchronization. International Journal of Engineering & Extended Technologies Research, 5(6), 7774–7779.
