gke-cost

GKE Cost Optimization

This reference covers strategies for reducing GKE costs while maintaining the golden path security and reliability posture.

MCP Tools: get_k8s_resource, describe_k8s_resource, apply_k8s_manifest, patch_k8s_resource, get_cluster

Golden Path Cost Features

The golden path already includes cost-optimizing settings:

: : : scale-down reduces idle : : : : compute : | verticalPodAutoscaling | enabled | VPA recommendations | : : : prevent : : : : over-provisioning : | Autopilot pricing | Pay per pod request | No charge for unused | : : : node capacity : | Node Auto Provisioning | enabled | Right-sized node pools | : : : created automatically :

Cost Optimization Strategies

1. Spot VMs via ComputeClasses

Use Spot VMs for fault-tolerant workloads (60-90% cost reduction).

apiVersion: cloud.google.com/v1
kind: ComputeClass
metadata:
  name: spot-with-fallback
spec:
  activeMigration:
    optimizeRulePriority: true
  priorities:
  - machineFamily: n4
    spot: true
  - machineFamily: n4
    spot: false

Spot-suitable workloads:

Workload | Spot-Suitable? --------------------------------- | --------------- Batch / data processing | Yes Dev / test environments | Yes Stateless web/API (replicas >= 2) | Yes (with PDBs) Jobs with checkpointing | Yes Stateful workloads (databases) | No Single-replica critical services | No

Handling eviction:

spec:
  template:
    spec:
      terminationGracePeriodSeconds: 25  # Must be < 30s for Spot
      containers:
      - name: app
        lifecycle:
          preStop:
            exec:
              command: ["/bin/sh", "-c", "sleep 5"]

1.1 Direct Workload Spot Selection (NodeSelector)

For stateless or batch workloads in GKE Autopilot, you do not need to create a custom ComputeClass to use Spot VMs. You can target the built-in Spot capacity directly by adding a nodeSelector to your Pod or Deployment spec.

[!WARNING] Preemption Warning: Spot VMs are interruptible and can be preempted at any time with a 30-second notice. Workloads must be fault-tolerant and run with at least 2 replicas for high availability. Always explicitly warn users about this preemption risk when recommending Spot VMs.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: stateless-spot-app
spec:
  replicas: 2
  template:
    spec:
      nodeSelector:
        cloud.google.com/gke-provisioning: Spot
      terminationGracePeriodSeconds: 25  # Must be < 30s to allow graceful shutdown before preemption
      containers:
      - name: app
        image: <IMAGE>

2. Pod Rightsizing

Use VPA recommendations to reduce over-provisioned requests.

# 1. Deploy VPA in recommendation mode
kubectl apply -f - <<EOF
apiVersion: autoscaling.k8s.io/v1
kind: VerticalPodAutoscaler
metadata:
  name: <DEPLOYMENT>-vpa
spec:
  targetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: <DEPLOYMENT>
  updatePolicy:
    updateMode: "Off"
EOF

# 2. Wait 24+ hours for data collection

# 3. Read recommendations
kubectl get vpa <DEPLOYMENT>-vpa -o jsonpath='{.status.recommendation}'

Optimization rules:

Condition | Action | Savings ----------------------------- | ---------------------------------- | ------- CPU request >5x P95 actual | Reduce to P95 1.2 | High Memory request >3x P95 actual | Reduce to P95 1.2 | High CPU request >2x P95 actual | Reduce to P95 * 1.2 | Medium No resource requests set | Add requests (enables bin-packing) | Medium

3. Machine Type Selection

: : optimized : : | n4a | Axion Arm-based, general-purpose | Low | : : price-performance : : | n4 / n4d | General purpose (Intel/AMD), flexible shapes | Low-Medium | | c4a | Compute-optimized (Arm), high efficiency | Medium-High | | c3 / c4 | Compute-optimized (Intel) | Medium-High | | c3d / c4d | Compute-optimized (AMD), high-performance | Medium-High | : : throughput : : | ek-standard | Autopilot enhanced (golden path) | Medium | | m3 / x4 | Memory-optimized, SAP HANA, large databases | High | | g2 (L4 GPU) | AI inference | High | | a3 (H100 GPU) | AI training | Highest | | a4 / a4x | Ultra-scale AI (Blackwell GPUs) | Highest |

In Autopilot, machine type is managed. Use ComputeClasses to influence selection.

4. Committed Use Discounts (CUDs)

For steady-state workloads, purchase 1-year or 3-year CUDs:

• 1-year: ~20-30% discount
• 3-year: ~50-55% discount
• Applied automatically to matching usage in the region
• Purchase via Google Cloud Console > Billing > Committed use discounts

5. Cluster Management

• Stop/start dev clusters: Idle dev clusters cost money even with no

workloads (control plane fee).

• Right-size node pools (Standard): Use Cluster Autoscaler with

appropriate min/max.

• Multi-tenant clusters: Share a single cluster across teams instead of

per-team clusters (see the gke-multitenancy skill).

Cost Monitoring

# View cluster cost breakdown (requires Cost Management API)
gcloud billing budgets list --billing-account=<BILLING_ACCOUNT> --quiet

# View node utilization
kubectl top nodes

# View pod resource usage vs requests
kubectl top pods --all-namespaces --containers

Dev/Test Cost Savings

For non-production environments, these golden path deviations are acceptable:

| Setting | Production (Golden | Dev/Test | : : Path) : : | ----------------------- | ------------------ | ----------------------------- | | Cluster mode | Autopilot | Autopilot (cheaper with fewer | : : : pods) : | Release channel | Regular | Rapid (get fixes faster) | | Private nodes | Required | Optional (simpler access) | | Monitoring components | Full suite | SYSTEM_COMPONENTS only | | Secret Manager rotation | 120s | Disabled | | Maintenance windows | Configured | Not needed |