gke-cost

name	gke-cost
description	Optimizes GKE costs, rightsizes workloads, and configures Spot VMs and CUDs. Use when optimizing GKE costs, rightsizing GKE workloads, or configuring GKE Spot VMs. 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 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:

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 :

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

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-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 |