Gke Cost Optimization

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

• Enable Cost Allocation: Always enable GKE cost allocation

(--enable-cost-allocation) to gain billing transparency across namespaces

and labels.

• Enforce Resource Quotas: Restrict namespace CPU/memory limits in

multi-tenant environments to prevent runaway costs or noisy neighbors.

• Rightsize Continuously: Run VPA in recommendation mode (`updateMode:

Off) and adjust requests to match P95 * 1.2`.

• Leverage Spot VMs: Use Spot VMs with nodeSelector or ComputeClass

for stateless, fault-tolerant workloads to save 60-90%.

• Optimize Autoscaling Profile: Use OPTIMIZE_UTILIZATION for aggressive

node scale-down on idle compute.

• Consolidate & Clean Up: Stop idle development clusters and consolidate

multi-team workloads into shared multi-tenant clusters.