Cloud Cost Optimization Strategies for Kubernetes-Based Applications

Abstract

As Kubernetes adoption accelerates in enterprise environments, managing and optimizing cloud costs for containerized applications has become increasingly important. This paper presents a systematic analysis of cost optimization strategies for Kubernetes workloads across major cloud platforms—AWS (EKS), Azure (AKS), and Google Cloud (GKE). We categorize cost drivers into compute overprovisioning, underutilized persistent volumes, inefficient autoscaling policies, and opaque network egress charges. To evaluate the effectiveness of mitigation techniques, we deploy a microservices-based application with variable traffic patterns and apply strategies such as vertical pod autoscaling, bin-packing-aware node scheduling, spot instance integration, and request/limit calibration. Results show that optimized resource requests and node pool configurations reduce compute costs by up to 37%. Additionally, using preemptible instances for stateless services and implementing custom metrics for autoscalers yields further savings without compromising performance. We also explore open-source cost monitoring tools like Kubecost and Kubevious to track real-time expenses and alert on anomalies. Challenges remain in managing multi-cluster environments and predicting dynamic traffic patterns. This paper offers a cost-aware deployment framework for DevOps teams and provides a decision matrix for balancing availability, resilience, and budget constraints. Our recommendations support sustainable cloud adoption in production Kubernetes environments.