Kubernetes Cost Optimization: Essential Guide for K8s Savings

Kubernetes has revolutionized how we deploy and manage containerized applications, but it's also created new cost optimization challenges. Studies show that 60-70% of Kubernetes resources are wasted due to overprovisioning. This guide covers essential strategies to optimize your K8s spending.

Why Kubernetes Costs Are Hard to Control

Kubernetes cost optimization is uniquely challenging because:

• Abstraction layers: Pods, nodes, and clusters obscure actual resource consumption
• Overprovisioning culture: Teams request more resources than needed "just in case"
• Dynamic workloads: Resource needs change constantly throughout the day
• Multi-tenancy: Shared clusters make cost attribution complex

Resource Requests and Limits

1. Right-Size Resource Requests

Resource requests determine scheduling and directly impact costs. Analyze actual CPU and memory usage with tools like Prometheus and set requests to match the 95th percentile of actual usage.

# Example: Right-sized resource configuration
resources:
  requests:
    cpu: "100m"      # Based on actual usage analysis
    memory: "256Mi"
  limits:
    cpu: "500m"      # Allow bursting for peak loads
    memory: "512Mi"

2. Implement Vertical Pod Autoscaler (VPA)

VPA automatically adjusts resource requests based on actual usage. It analyzes historical data and recommends or automatically applies optimal resource configurations.

3. Set Appropriate Limits

Limits prevent runaway resource consumption but shouldn't be set too tight. A good rule: set CPU limits at 2-5x requests and memory limits at 1.5-2x requests.

Cluster Autoscaling

4. Enable Cluster Autoscaler

Cluster Autoscaler automatically adjusts the number of nodes based on pending pods and resource utilization. This ensures you only pay for capacity when needed.

5. Configure Scale-Down Settings

Tune scale-down settings to balance responsiveness with stability. Consider setting scale-down-delay-after-add to prevent thrashing and scale-down-utilization-threshold based on your workload patterns.

6. Use Horizontal Pod Autoscaler (HPA)

HPA scales pods based on CPU, memory, or custom metrics. Combined with Cluster Autoscaler, this creates a fully dynamic infrastructure that scales with demand.

Node Optimization

7. Choose the Right Instance Types

Match node instance types to your workload characteristics. CPU-intensive workloads benefit from compute-optimized instances, while memory-heavy applications need memory-optimized nodes.

8. Leverage Spot/Preemptible Nodes

Spot instances (AWS), Preemptible VMs (GCP), or Spot VMs (Azure) offer 60-90% savings for fault-tolerant workloads. Use node pools with taints and tolerations to safely run workloads on spot nodes.

9. Optimize Node Utilization

Aim for 70-80% node utilization. Lower utilization means wasted resources; higher utilization risks performance issues and scheduling failures.

Namespace and Workload Management

• 10. Implement ResourceQuotas: Set namespace-level limits to prevent runaway costs
• 11. Use LimitRanges: Define default requests/limits for pods without specifications
• 12. Schedule Non-Critical Workloads Off-Peak: Run batch jobs during low-traffic periods
• 13. Implement Pod Disruption Budgets: Allow safe node scaling without service impact

Cost Visibility and Governance

14. Implement Cost Allocation

Use labels and namespaces for cost attribution. Track costs by team, application, and environment to drive accountability and identify optimization opportunities.

15. Set Up Cost Monitoring

Deploy cost monitoring tools to track spending trends, identify anomalies, and forecast future costs. Real-time visibility enables proactive optimization.