/gke-cost-optimization

name: gke-cost-optimization
description: >-
  Optimizes GKE costs, rightsizes workloads, and configures Spot VMs, CUDs, cost
  allocation, and resource quotas. Use when optimizing GKE cluster or workload
  costs, configuring GKE cost allocation or quotas, rightsizing CPU/memory
  requests, or selecting Spot VMs and machine types. Don't use for general
  compute class provisioning or GPU Selection (use gke-compute-classes instead).
metadata:
  category: CloudObservabilityAndMonitoring

GKE Cost Optimization

This reference covers strategies and workflows for reducing Google Kubernetes Engine (GKE) costs while maintaining a secure and reliable 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:

| Setting | Value | Impact | | ------------------------ | ---------------------- | ----------------------- | | `autoscalingProfile` | `OPTIMIZE_UTILIZATION` | Aggressive node | : : : 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 :

Workflows & Optimization Strategies

1. Prerequisite: Cost Allocation & Monitoring

To enable GKE cost allocation (`--enable-cost-allocation`) for billing tracking across namespaces and labels, inspect live cluster utilization (`kubectl top`), or run historical cost breakdown queries in BigQuery (`bq`), use the **`gke-cost-analysis`** skill. Once tracking is active and waste is diagnosed, apply the optimization workflows below.

2. Configure Resource Quotas

Resource quotas restrict total resource consumption across tenants in multi-tenant clusters, preventing runaway costs.

kubectl apply -f - <<EOF
apiVersion: v1
kind: ResourceQuota
metadata:
  name: compute-quota
  namespace: {namespace}
spec:
  hard:
    requests.cpu: "4"
    requests.memory: 16Gi
    limits.cpu: "8"
    limits.memory: 32Gi
EOF

3. Pod Rightsizing (VPA & MPA)

Adjust pod resource requests to match actual utilization. Over-provisioned requests are one of the largest sources of waste.

• **Use VPA in Recommendation Mode**:

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

# 2. Wait 24+ hours for data collection

# 3. Read recommendations
kubectl get vpa {deployment_name}-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

• **Use MPA**: Reconcile HPA and VPA recommendations when scaling both

horizontally and vertically to avoid conflicting scale events.

• **Review Cost Recommendations**: Check Google Cloud Console (`Cost

Management` > `GKE Cost Optimization`) for built-in rightsizing suggestions.

4. Spot VMs via ComputeClasses & NodeSelector

Use Spot VMs for fault-tolerant workloads to achieve 60-90% cost reduction.

4.1 ComputeClass Configuration

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

4.2 Direct Workload Spot Selection (`nodeSelector`)

For stateless or batch workloads in GKE Autopilot, target Spot capacity directly using `nodeSelector`:

> [!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 for Spot preemption handling
      containers:
      - name: app
        image: {image_name}
        lifecycle:
          preStop:
            exec:
              command: ["/bin/sh", "-c", "sleep 5"]

**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

5. Machine Type Selection

When choosing node shapes or configuring ComputeClasses:

| Family | Use Case | Relative Cost |