Best practice for Running Cost Optimized Kubernetes Applications on GKE

Kubernetes has transformed the way applications are deployed and managed at scale. But as organizations embrace Kubernetes, particularly through managed services like Google Kubernetes Engine (GKE), they face a common challenge: rising cloud costs.

Table of Contents

1. Introduction

2. Why Cost Optimization Matters on GKE

3. Core Cost Drivers in Kubernetes on GKE

4. Best Practices for GKE Cost Optimization

   Right-Sizing Workloads

   Autoscaling (Cluster & Pod)

   Choosing the Right Machine Types

   Preemptible VMs and Autopilot Mode

   Resource Requests and Limits

   Efficient Use of Persistent Storage

   Namespace and Labeling Strategy

   Idle Resource Management

   Cost-Aware CI/CD Pipeline

   Monitoring and Cost Visibility Tools

5. Budgeting and Alerts with GCP

6. Real-World Case Study: How a SaaS Team Cut Costs by 40%

7. Future Trends: FinOps in Kubernetes

8. Conclusion

9. FAQs

1. Introduction

Kubernetes has transformed the way applications are deployed and managed at scale. But as organizations embrace Kubernetes, particularly through managed services like Google Kubernetes Engine (GKE), they face a common challenge: rising cloud costs.

This guide walks you through Kubernetes cost optimization on GKE, providing actionable strategies to reduce your spend without sacrificing performance or scalability.

2. Why Cost Optimization Matters on GKE

Google Kubernetes Engine (GKE) offers flexibility and scalability but comes at a price. Without careful planning, costs can quickly spiral out of control due to underutilized resources, poor workload planning, or misconfigured clusters.

Key reasons to optimize:

• Prevent budget overruns

• Improve resource utilization

• Increase ROI on your cloud investment

• Build sustainable and scalable infrastructure

3. Core Cost Drivers in Kubernetes on GKE

Before diving into solutions, let’s understand the primary cost drivers:

• Node usage (vCPUs, memory, GPU)

• Persistent volumes

• Load balancers and ingress controllers

• Networking egress charges

• Autoscaling misconfigurations

4. Best Practices for GKE Cost Optimization

🔹 Right-Sizing Workloads

Over-provisioning is one of the biggest cost culprits. Use real-time monitoring tools to analyze actual CPU and memory usage, then adjust requests and limits accordingly.

Tools: Prometheus, GKE Metrics Server, Goldilocks

🔹 Enable Autoscaling

• Cluster Autoscaler: Automatically adjusts the number of nodes.

• Horizontal Pod Autoscaler (HPA): Scales pods based on CPU/memory.

• Vertical Pod Autoscaler (VPA): Adjusts container resource limits dynamically.

Tip: Combine HPA + VPA + Cluster Autoscaler for intelligent scaling.

🔹 Choose the Right Machine Type

Use custom machine types to avoid overpaying for generic resources. If workloads are memory-intensive but not CPU-bound, configure VM shapes accordingly.

Tip: Use cost-effective E2 instances for test/staging clusters.

🔹 Use Preemptible VMs or Autopilot Mode

• Preemptible VMs are 80% cheaper but short-lived. Ideal for stateless, fault-tolerant workloads.

• GKE Autopilot abstracts away node management and charges per pod, reducing idle costs.

🔹 Set Proper Resource Requests and Limits

Avoid default settings. Set requests based on average usage and limits to cap unexpected spikes.

Bad practice: Setting both too high wastes resources. Too low can lead to pod eviction.

🔹 Efficient Use of Persistent Storage

• Use SSD only when necessary (e.g., low-latency DBs)

• Regularly clean up unused Persistent Volume Claims (PVCs)

• Use filestore for shared workloads instead of duplicating volumes

🔹 Implement Namespace and Labeling Strategy

Namespaces and labels make it easier to allocate and analyze cost per team, environment, or service.

Use tools like Kubecost or GCP Cost Allocation to link costs with labels.

🔹 Detect and Manage Idle Resources

Stale pods, services, and namespaces accumulate cost over time. Schedule regular cleanups.

Use: kubectl top pods, kubectl get all –all-namespaces + custom scripts.

🔹 Cost-Aware CI/CD Pipelines

• Run build/test jobs on preemptible nodes

• Use Ephemeral environments that auto-shutdown after PR testing

• Avoid keeping preview apps running indefinitely

🔹 Use Monitoring and Visibility Tools

• GCP Cloud Monitoring for node/pod metrics

• Kubecost for workload-level cost visibility

• GKE Usage Metering to see which workloads consume most resources

5. Budgeting and Alerts with GCP

GCP offers powerful tools for setting budgets and monitoring cost thresholds:

• Set budgets per project or per label

• Enable alerting on budget thresholds

• Use Billing Exports + BigQuery for custom reports

6. Real-World Case Study: Cutting Costs by 40%

A mid-size SaaS company running a multitenant B2B app on GKE optimized their clusters by:

• Switching dev/staging workloads to preemptible nodes

• Reducing CPU requests by 30% using Goldilocks recommendations

• Removing stale PVCs (~200GB)

• Moving from N1 to E2 machine types

• Implementing HPA across all microservices

Result: Cloud spend dropped by 40% over 2 months without user impact.

7. Future Trends: FinOps for Kubernetes

As cloud-native adoption grows, so doe s the need for FinOps — financial operations for engineering. GKE-native FinOps will:

• Integrate cost metrics into CI/CD workflows

• Influence design-time decisions for engineers

• Automate cost guardrails based on GitOps policies

8. Conclusion

Optimizing Kubernetes costs on GKE is not a one-time task — it’s a continuous process of monitoring, right-sizing, and aligning infrastructure with application needs.

By adopting these best practices and tools, your team can achieve greater performance at lower costs and build a culture of cost-conscious engineering.

9. FAQs

1. How can I monitor costs in GKE?

Use GKE Usage Metering, Kubecost, or GCP Billing exports for workload-level insights.

2. What’s the difference between Autopilot and Standard GKE?

Autopilot manages infrastructure and charges per pod, while Standard GKE gives full control over nodes.

3. Can I use spot instances in GKE?

Yes, preemptible VMs (GCP’s version of spot) work well for stateless workloads.

4. What’s the best way to avoid over-provisioning?

Use tools like Goldilocks and VPA to analyze and right-size requests/limits.

5. Are there cost implications for Load Balancers in GKE?

Yes, especially with Ingress and NodePorts. Use internal LBs or shared services if possible.

6. How does GKE autoscaling work?

Cluster Autoscaler adjusts node count; HPA scales pods based on metrics; VPA adjusts pod resource settings.

7. Should I set resource limits for every pod?

Yes. Without limits, a rogue pod can exhaust node resources and trigger eviction.

8. Is Autopilot mode cheaper than Standard?

Depends. For small, bursty workloads, Autopilot is often cheaper due to per-pod billing.

9. How do I find unused Kubernetes resources?

Use CLI commands and tools like K9s, Lens, or custom scripts to detect idle pods/services.

10. What tools help with Kubernetes cost visibility?

Kubecost, GCP Billing Export + BigQuery, and GKE Usage Metering are most effective.