GKE Cost Optimization

Purpose

Reduce GKE spending while maintaining performance and reliability. This skill covers cost analysis, resource right-sizing, cluster mode comparison, and budget monitoring strategies.

When to Use

Use this skill when you need to:

• Analyze GKE spending and identify cost optimization opportunities
• Compare Autopilot vs Standard billing models for your workload
• Right-size pod resource requests and limits
• Configure Horizontal/Vertical Pod Autoscaling to reduce waste
• Set up Spot VMs for batch or non-critical workloads
• Create budget alerts and track cost per service
• Optimize resource utilization

Trigger phrases: “optimize GKE costs”, “reduce Kubernetes spending”, “right-size resources”, “Autopilot vs Standard pricing”, “GKE budget alerts”

Table of Contents

• Purpose
• When to Use
• Quick Start
• Instructions
  • Step 1: Understand Your Billing Model
  • Step 2: Analyze Current Resource Usage
  • Step 3: Right-Size Pod Resources
  • Step 4: Configure Horizontal Pod Autoscaling
  • Step 5: Use Spot VMs
  • Step 6: Set Up Cost Monitoring
  • Step 7: Analyze Cost by Namespace/Service
• Examples
• Requirements
• See Also

Quick Start

Analyze and optimize costs in three steps:

# 1. Check current resource usage
kubectl top pods -n wtr-supplier-charges

# 2. Analyze resource requests vs actual usage
kubectl describe deployment supplier-charges-hub -n wtr-supplier-charges | grep -A 10 "resources:"

# 3. Compare Autopilot vs Standard pricing for your workload
# Autopilot: Pay per pod per second for requests
# Standard: Pay per provisioned node per hour (regardless of usage)

Instructions

Step 1: Understand Your Billing Model

GKE Autopilot (Per-Pod Billing – Recommended)

Cost Calculation:

Monthly Cost = (vCPU requests * $0.04 + Memory requests GB * $0.004 + Disk GB * $0.0001) * seconds per month

Example for Supplier Charges Hub:

• 2 replicas, each requesting: 1 vCPU + 2 GB memory
• Monthly cost ≈ (2 * 1 * $0.04 + 2 * 2 * $0.004) * 2.592M seconds ≈ $260

Advantages:

• Pay only for what pods request (not what entire cluster capacity is)
• No idle resource costs
• Perfect for variable workloads (scales up/down automatically)
• Up to 60% cheaper than Standard for typical workloads

GKE Standard (Node Billing)

Cost Calculation:

Monthly Cost = Number of nodes * Machine type hourly rate * 730 hours per month

Example for Supplier Charges Hub:

• 3 n2-standard-4 nodes (standard pool) = ~$400/month
• Even if pods use only 30% of capacity, you pay for 100%

Advantages:

• Predictable costs (great for committed use discounts)
• Full control over infrastructure
• Better for stable, high-utilization workloads

Step 2: Analyze Current Resource Usage

Check if pods are over-provisioned:

# View actual vs requested resources
kubectl top pods -n wtr-supplier-charges -o wide

# Compare to requests
kubectl get pods -n wtr-supplier-charges -o jsonpath='{.items[*].spec.containers[*].resources.requests}'

Analysis Questions:

• Are actual values significantly lower than requests?
• Is memory usage consistently below 75% of limits?
• Is CPU usage consistently below 70% of requests?

If Yes → Right-size resources (reduce requests)

Step 3: Right-Size Pod Resources

Use Vertical Pod Autoscaler (VPA) recommendations:

# Apply VPA to deployment
cat <<EOF | kubectl apply -f -
apiVersion: autoscaling.k8s.io/v1
kind: VerticalPodAutoscaler
metadata:
  name: supplier-charges-hub-vpa
  namespace: wtr-supplier-charges
spec:
  targetRef:
    apiVersion: "apps/v1"
    kind: Deployment
    name: supplier-charges-hub
  updatePolicy:
    updateMode: "Off"  # Only provide recommendations, don't auto-update
  resourcePolicy:
    containerPolicies:
    - containerName: supplier-charges-hub-container
      minAllowed:
        cpu: 500m
        memory: 1Gi
      maxAllowed:
        cpu: 2
        memory: 4Gi
EOF

# Wait 1 week for data collection, then view recommendations
kubectl describe vpa supplier-charges-hub-vpa -n wtr-supplier-charges | grep -A 20 "Recommendation"

Recommended VPA Values:

resources:
  requests:
    cpu: 500m        # Reduced from 1000m if usage averages 300m
    memory: 1.5Gi    # Reduced from 2Gi if usage averages 1Gi
  limits:
    cpu: 500m        # Match requests for Guaranteed QoS
    memory: 1.5Gi

Step 4: Configure Horizontal Pod Autoscaling (HPA)

Let Kubernetes scale replicas based on demand:

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: supplier-charges-hub-hpa
  namespace: wtr-supplier-charges
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: supplier-charges-hub
  minReplicas: 1          # Scale down to 1 during low traffic
  maxReplicas: 5          # Scale up to 5 during peak
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 80
  behavior:
    scaleDown:
      stabilizationWindowSeconds: 300
      policies:
      - type: Percent
        value: 50
        periodSeconds: 60

Cost Savings: Reduces off-peak replicas from 2 to 1 = ~30% savings if traffic varies.

Step 5: Use Spot VMs (GKE Standard Only)

For non-critical or fault-tolerant workloads, use Spot VMs (91% discount):

# Create Spot node pool
gcloud container node-pools create spot-pool \
  --cluster=shared-gke-standard-01-euw2 \
  --region=europe-west2 \
  --spot \
  --machine-type=n2-standard-4 \
  --num-nodes=2 \
  --enable-autoscaling \
  --min-nodes=0 \
  --max-nodes=10

# Add toleration to workloads that can run on Spot
tolerations:
- key: cloud.google.com/gke-spot
  operator: Equal
  value: "true"
  effect: NoSchedule

Use Cases: Batch processing, CI/CD build workers, non-critical background jobs

Not for: Production APIs (Supplier Charges Hub should NOT use Spot)

Step 6: Set Up Cost Monitoring

Create budget alerts:

# Create budget with 50%, 90%, 100% thresholds
gcloud billing budgets create \
  --billing-account=BILLING_ACCOUNT_ID \
  --display-name="GKE Labs Environment" \
  --budget-amount=500USD \
  --threshold-rule=percent=50 \
  --threshold-rule=percent=90 \
  --threshold-rule=percent=100 \
  --notification-rule-name=email-alert

Step 7: Analyze Cost by Namespace/Service

Export billing data for detailed analysis:

# Tag namespace for cost tracking
kubectl label namespace wtr-supplier-charges \
  cost-center=supplier-charges \
  environment=labs

# Later, filter GCP Billing reports by labels
# In Cloud Console: Billing → Reports → Filter by labels

Examples

Example 1: Optimize Supplier Charges Hub Deployment

#!/bin/bash
# Step-by-step optimization

DEPLOYMENT="supplier-charges-hub"
NAMESPACE="wtr-supplier-charges"

echo "=== GKE Cost Optimization ==="

echo ""
echo "1. Current Resource Usage:"
kubectl top pods -l app=$DEPLOYMENT -n $NAMESPACE

echo ""
echo "2. Current Resource Requests:"
kubectl get deployment $DEPLOYMENT -n $NAMESPACE \
  -o jsonpath='{.spec.template.spec.containers[0].resources}'

echo ""
echo "3. Applying VPA for recommendations:"
cat <<EOF | kubectl apply -f -
apiVersion: autoscaling.k8s.io/v1
kind: VerticalPodAutoscaler
metadata:
  name: ${DEPLOYMENT}-vpa
  namespace: $NAMESPACE
spec:
  targetRef:
    apiVersion: "apps/v1"
    kind: Deployment
    name: $DEPLOYMENT
  updatePolicy:
    updateMode: "Off"
EOF

echo "VPA created. Wait 1 week for recommendations."

echo ""
echo "4. Applying HPA for automatic scaling:"
cat <<EOF | kubectl apply -f -
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: ${DEPLOYMENT}-hpa
  namespace: $NAMESPACE
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: $DEPLOYMENT
  minReplicas: 1
  maxReplicas: 5
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
EOF

echo "HPA created. Replicas will auto-scale based on demand."

echo ""
echo "5. Estimated Savings:"
echo "   - Autopilot: ~$260/month (current)"
echo "   - With HPA (avg 1.5 replicas): ~$195/month"
echo "   - Savings: ~$65/month (25%)"

Example 2: Autopilot vs Standard Cost Comparison

#!/bin/bash
# Compare pricing for your workload

# Supplier Charges Hub specifications
REPLICAS=2
CPU_PER_POD="1000m"
MEMORY_PER_POD="2Gi"
DISK_PER_POD="10Gi"

# Autopilot pricing (europe-west2)
CPU_PRICE=0.04
MEMORY_PRICE=0.004
DISK_PRICE=0.0001
SECONDS_PER_MONTH=2592000

TOTAL_CPU=$(echo "$REPLICAS * 1" | bc)
TOTAL_MEMORY=$(echo "$REPLICAS * 2" | bc)
TOTAL_DISK=$(echo "$REPLICAS * 10" | bc)

AUTOPILOT_COST=$(echo "($TOTAL_CPU * $CPU_PRICE + $TOTAL_MEMORY * $MEMORY_PRICE + $TOTAL_DISK * $DISK_PRICE) * $SECONDS_PER_MONTH / 3600 / 24 / 30" | bc)

echo "=== Cost Comparison: Autopilot vs Standard ==="
echo ""
echo "Workload: $REPLICAS replicas, ${CPU_PER_POD}m CPU, ${MEMORY_PER_POD} memory each"
echo ""
echo "AUTOPILOT (Per-Pod Billing):"
echo "  Monthly cost: ~\$${AUTOPILOT_COST}"
echo ""
echo "STANDARD (Node-Based Billing):"
echo "  3x n2-standard-4 nodes @ \$400/month"
echo "  3x cloud-sql-proxy sidecars @ \$50/month"
echo "  Total: ~\$450/month"
echo ""
echo "SAVINGS with Autopilot: ~\$190/month (42%)"

Example 3: Set Up Cost Tracking and Alerts

#!/bin/bash
# Set up cost monitoring

BILLING_ACCOUNT="000000-A1B2C3-D4E5F6"  # Replace with your billing account
NAMESPACE="wtr-supplier-charges"
PROJECT_ID="ecp-wtr-supplier-charges-labs"

echo "=== Setting Up Cost Monitoring ==="

echo ""
echo "1. Tag resources for cost tracking"
kubectl label namespace $NAMESPACE \
  cost-center=supplier-charges \
  environment=labs \
  --overwrite

echo ""
echo "2. Create billing budget"
gcloud billing budgets create \
  --billing-account=$BILLING_ACCOUNT \
  --display-name="GKE Labs - Supplier Charges Hub" \
  --budget-amount=500USD \
  --threshold-rule=percent=50 \
  --threshold-rule=percent=90 \
  --threshold-rule=percent=100 \
  --notification-rule-name="Budget Alert"

echo ""
echo "3. Enable Cost Analysis"
echo "   Go to: https://console.cloud.google.com/billing/reports"
echo "   Filter by: labels.cost-center = supplier-charges"

echo ""
echo "4. Track GKE Costs"
echo "   Go to: https://console.cloud.google.com/monitoring/dashboards"
echo "   Create dashboard filtering: resource.type=k8s_cluster AND resource.labels.cluster_name"

Requirements

• GKE cluster running (Autopilot or Standard)
• kubectl access to view pod metrics and deployments
• gcloud CLI access for billing and monitoring setup
• Metrics server enabled (usually on by default)
• For billing: access to GCP billing account

See Also

• gcp-gke-cluster-setup – Understand cluster modes and their costs
• gcp-gke-deployment-strategies – Optimize deployment replicas
• gcp-gke-monitoring-observability – Monitor resource utilization