Optimizing Enterprise Infrastructure Through AI-Driven Kubernetes Autoscaling and Predictive Cloud Cost Analytics

Abstract

The rapid adoption of cloud-native technologies has increased the complexity of enterprise infrastructure management, creating a need for intelligent resource allocation and cost optimization. Traditional infrastructure management approaches often rely on static resource configurations, reactive scaling policies, and retrospective cost analysis, which may lead to resource wastage, performance degradation, and unpredictable operational expenditure. This study explores the integration of artificial intelligence (AI)-driven Kubernetes autoscaling and predictive cloud cost analytics as a unified strategy for improving enterprise infrastructure efficiency. The proposed approach combines machine learning-based workload forecasting, dynamic resource allocation, container orchestration, and predictive financial analytics to align computing resources with changing application demands. Kubernetes metrics, historical utilization records, application performance indicators, and cloud billing information serve as inputs for predictive models that estimate workload variations and future infrastructure expenditure. These predictions support proactive scaling decisions, resource rightsizing, and cost-aware workload scheduling while maintaining service-level objectives. The methodology incorporates quantitative performance evaluation using resource utilization, response time, scaling latency, prediction accuracy, and infrastructure cost as principal indicators. By integrating operational intelligence with financial forecasting, enterprises can develop a more adaptive and transparent infrastructure management framework. The study provides a conceptual and methodological foundation for evaluating AI-assisted cloud optimization and highlights the importance of reliability, explainability, governance, and continuous model improvement in enterprise Kubernetes environments.