GKE Cost Optimization: The Complete Guide to Reducing Kubernetes Costs
Google Kubernetes Engine (GKE) costs can quickly spiral out of control. Between cluster management fees, node compute costs, and network charges, optimizing GKE spend requires a comprehensive strategy. This guide shows you how to reduce GKE costs by up to 60%.
Understanding GKE Pricing
GKE pricing has several components that contribute to your total cost:
- Cluster management fee: $0.10/hour for Standard clusters (~$73/month)
- Node compute: Based on machine type and size
- Persistent storage: $0.04-0.17/GB/month depending on type
- Network egress: $0.12+/GB for internet traffic
- Load balancers: $0.025/hour plus data processing
1. Choose the Right Cluster Mode
GKE Autopilot vs Standard
The choice between Autopilot and Standard can significantly impact costs:
GKE Standard
- • $0.10/hour cluster fee
- • Pay for full node capacity
- • Better for consistent workloads
- • More control over configuration
GKE Autopilot
- • No cluster management fee
- • Pay only for pod resources
- • Better for variable workloads
- • Google manages infrastructure
Recommendation
Use Autopilot for dev/test environments and variable workloads (up to 40% savings). Use Standard for production with high, consistent utilization.
2. Optimize Node Pools
Right-size Your Machines
Choosing the right machine type is crucial for cost optimization:
- E2 series: Best for general workloads, 30% cheaper than N2
- N2D series: AMD-based, 10-15% cheaper than Intel N2
- Custom machine types: Pay for exactly what you need
Enable Cluster Autoscaling
Cluster autoscaler automatically adjusts node count based on workload:
Best Practices
- • Set appropriate min/max node counts
- • Use node auto-provisioning for optimal machine selection
- • Configure scale-down delay for stability (default 10 min)
- • Enable scale-down utilization threshold (default 50%)
3. Use Spot VMs
Spot VMs offer up to 91% discount for fault-tolerant workloads:
- Use for batch processing, CI/CD, and stateless services
- Implement proper pod disruption budgets
- Use node affinity to mix Spot and on-demand nodes
- Spread across multiple zones for availability
Savings Potential
Up to 91% savings on compute costs with Spot VMs
4. Committed Use Discounts
For predictable workloads, committed use discounts offer significant savings:
- 1-year commitment: Up to 37% discount
- 3-year commitment: Up to 57% discount
- Flex commitments: Apply to any region
5. Pod-Level Optimization
Set Resource Requests and Limits
Proper resource configuration is essential for efficient bin-packing:
- Always set requests based on actual usage
- Use Vertical Pod Autoscaler (VPA) for recommendations
- Set limits to prevent runaway resource usage
- Use LimitRanges to enforce best practices
Enable Horizontal Pod Autoscaling
HPA automatically adjusts pod replicas based on metrics:
- Scale based on CPU, memory, or custom metrics
- Set appropriate min/max replicas
- Consider KEDA for event-driven scaling
6. Storage Optimization
Storage costs often go unnoticed but can be significant:
- Standard PD: $0.04/GB - Use for non-critical data
- Balanced PD: $0.10/GB - Good price/performance
- SSD PD: $0.17/GB - Only for high-IOPS needs
- Extreme PD: $0.19/GB - Rarely needed
- Use Standard PD for dev/test environments
- Implement volume snapshots instead of duplicate data
- Use regional PD only when required for HA
7. Network Optimization
Network costs can surprise you if not managed carefully:
- Use private clusters to reduce egress
- Enable Cloud CDN for public content
- Use Internal Load Balancers when possible
- Optimize cross-zone traffic with topology-aware routing