AI-Assisted FinOps and Resource Optimization for Kubernetes-Based Hybrid Cloud Platforms: A Predictive Framework for Reducing Infrastructure Cost While Maintaining Reliability and Performance

  • Authors

    • Bhanu Kiran Kumar Muggalla Computer Information System, University of Central Missouri, USA. Author

    DOI:

    https://doi.org/10.67228/30715717/IJDEIC-V9I4P101

    Published 10-03-2026

  • Artificial Intelligence, FinOps, Kubernetes, Hybrid Cloud, Cloud Cost Optimization, Resource Management, Predictive Analytics, Platform Engineering

    Issue

    Vol. 9 No. 4 (2026)

    Section

    Articles

  • Abstract

    The increasing adoption of Kubernetes-based hybrid cloud environments has introduced challenges in managing infrastructure costs, resource utilization, and service reliability. Traditional FinOps practices primarily rely on reactive cost monitoring, while Kubernetes-native optimization techniques respond to existing workload conditions rather than predicting future demands. This study proposes an AI-Assisted FinOps framework for predictive resource optimization in Kubernetes-based hybrid cloud platforms. The framework integrates workload forecasting, cloud cost intelligence, resource optimization, and reliability-aware decision-making to enable proactive infrastructure management. By combining artificial intelligence with FinOps principles, the proposed approach analyzes workload patterns, resource utilization, and cost-performance trade-offs across heterogeneous cloud environments. A design science research methodology is adopted to define the framework architecture, operational processes, and validation strategy. The evaluation considers cost efficiency, resource utilization, service reliability, and prediction accuracy using suitable datasets and benchmark environments. The proposed framework establishes a foundation for intelligent, predictive, and automated cloud cost optimization while maintaining application performance and operational resilience.

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