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
e2	General purpose, burstable	Lowest
t2a / t2d	Scale-out (Arm/AMD), price-performance	Low
: : 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 throughput	Medium-High
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

6. Committed Use Discounts (CUDs)

For steady-state workloads with predictable baseline usage, purchase 1-year or 3-year CUDs:

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

7. Cluster Management & Multi-Tenancy

• Stop/start dev clusters: Idle dev clusters cost money even with no workloads due to control plane fees.
• Right-size node pools (Standard): Use Cluster Autoscaler with appropriate min/max limits.
• Multi-tenant consolidation: Share a single cluster across multiple engineering teams instead of maintaining per-team clusters, using Namespaces and ResourceQuotas to isolate workloads.

Cost & Utilization Monitoring

To inspect live node/pod utilization (kubectl top nodes/pods), view cluster cost budgets (gcloud billing budgets list), or query detailed billing reports in BigQuery (bq query), refer to the gke-cost-analysis skill.

Dev/Test Cost Savings

For non-production environments, the following golden path deviations provide cost efficiency without impacting production safety:

| 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 |

Best Practices Summary

1. Enable Cost Allocation: Always enable GKE cost allocation (--enable-cost-allocation) to gain billing transparency across namespaces and labels.
2. Enforce Resource Quotas: Restrict namespace CPU/memory limits in multi-tenant environments to prevent runaway costs or noisy neighbors.
3. Rightsize Continuously: Run VPA in recommendation mode (updateMode: Off) and adjust requests to match P95 * 1.2.
4. Leverage Spot VMs: Use Spot VMs with nodeSelector or ComputeClass for stateless, fault-tolerant workloads to save 60-90%.
5. Optimize Autoscaling Profile: Use OPTIMIZE_UTILIZATION for aggressive node scale-down on idle compute.
6. Consolidate & Clean Up: Stop idle development clusters and consolidate multi-team workloads into shared multi-tenant clusters.