Cost Optimization Strategies for Large-Scale Kubernetes Clusters in Multi-Cloud Environments

Dinesh Kumar Movva

Abstract: Large-scale Kubernetes deployments across multi-cloud environments introduce compounding cost inefficiencies arising from workload over-provisioning, suboptimal node utilization, cross-cloud data transfer charges, and the absence of unified financial governance frameworks. Organizations operating Kubernetes at scale routinely waste 35?55% of their allocated cloud compute budget through misconfigured resource requests, idle node capacity, and reactive rather than proactive scaling strategies. This paper presents KCOS (Kubernetes Cost Optimization System), a multi-layered framework that integrates vertical pod autoscaling, bin-packing node consolidation, spot and preemptible instance arbitrage, and cross-cloud workload placement optimization to systematically reduce Kubernetes infrastructure costs without degrading workload performance or availability. KCOS is evaluated across four enterprise multi-cloud Kubernetes deployments spanning 1,200 to 4,800 nodes over a 24-week measurement period. The evaluation demonstrates that KCOS reduces mean infrastructure cost per workload unit by 38.4%, decreases idle node-hour waste by 61%, improves average node CPU utilization from 34% to 67%, and achieves a mean cost reduction of $2.1M annually per 1,000-node cluster. A multi-cloud cost governance framework, workload placement decision model, and Kubernetes-native implementation guide are presented, providing enterprise platform engineering teams with a structured path to production cost optimization.

Keywords: Kubernetes, Cost Optimization, Multi-Cloud, Container Orchestration, Resource Management, FinOps, Cloud Economics

How to Cite?: Dinesh Kumar Movva, "Cost Optimization Strategies for Large-Scale Kubernetes Clusters in Multi-Cloud Environments", Volume 15 Issue 5, May 2026, International Journal of Science and Research (IJSR), Pages: 1229-1235, https://www.ijsr.net/getabstract.php?paperid=SR26519080129, DOI: https://dx.doi.org/10.21275/SR26519080129

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