GKE Production Deployment

Deploy MCP Server LangGraph to Google Kubernetes Engine (GKE) Autopilot with production-grade security, observability, and high availability

Overview

This guide provides step-by-step instructions for deploying MCP Server LangGraph on Google Kubernetes Engine (GKE) Autopilot with enterprise-grade security, monitoring, and cost optimization.

Deployment Time

2-3 hours for complete production setup

Cost Savings

40-60% vs. traditional GKE Standard

Infrastructure Maturity

94/100 production-readiness score

Best Practices

100% GCP best practices compliance

What You’ll Deploy

• GKE Autopilot Cluster: Fully managed Kubernetes (regional, multi-zone)
• Cloud SQL PostgreSQL: High-availability database with automated backups
  • 3 databases: Keycloak (identity), OpenFGA (authorization), GDPR (compliance per ADR-0041)
• Memorystore Redis: High-availability cache with persistence
• Workload Identity: Secure pod-to-GCP service authentication
• Private Networking: VPC-native with Cloud NAT
• Observability: Cloud Monitoring, Logging, Trace, Profiler
• Security: Binary Authorization, Network Policies, Encryption

Key Benefits

40-60% Cost Savings

GKE Autopilot uses pay-per-pod pricing with no idle node costs. Production environment runs for $880-1,275/month vs.$1,290-1,970 with traditional GKE.

Zero Node Management

Google manages all node infrastructure, upgrades, and scaling automatically. Focus on your application, not Kubernetes operations.

99.9% Uptime

Regional deployment across 3 zones with automated failover for databases and cache provides enterprise-grade reliability.

Security by Default

Built-in Workload Identity, Binary Authorization, Shielded Nodes, Network Policies, and encryption at rest/transit.

---

Prerequisites

1. GCP Account & Project

   Create a GCP project with billing enabled:

   # Create project
   gcloud projects create PROJECT_ID --name="MCP LangGraph Production"
   
   # Link billing
   gcloud billing projects link PROJECT_ID --billing-account=BILLING_ACCOUNT_ID
   
   # Set active project
   gcloud config set project PROJECT_ID
   

2. Install Required Tools

   Tool	Version	Installation
   gcloud CLI	Latest	curl https://sdk.cloud.google.com | bash
   Terraform	≥ 1.5.0	terraform.io/downloads
   kubectl	≥ 1.28	gcloud components install kubectl
   kustomize	≥ 5.0	brew install kustomize

   Verify installations:

   gcloud version
   terraform version
   kubectl version --client
   kustomize version
   

3. Enable Required APIs

   Enable 20+ required GCP APIs:

   gcloud services enable \
       container.googleapis.com \
       compute.googleapis.com \
       sqladmin.googleapis.com \
       redis.googleapis.com \
       servicenetworking.googleapis.com \
       cloudresourcemanager.googleapis.com \
       iam.googleapis.com \
       binaryauthorization.googleapis.com \
       monitoring.googleapis.com \
       logging.googleapis.com \
       cloudtrace.googleapis.com \
       cloudprofiler.googleapis.com \
       secretmanager.googleapis.com \
       artifactregistry.googleapis.com \
       --project=PROJECT_ID
   

4. Configure IAM Permissions

   Required roles (or roles/owner):

   • roles/compute.networkAdmin
   • roles/container.admin
   • roles/cloudsql.admin
   • roles/redis.admin
   • roles/iam.securityAdmin
   • roles/resourcemanager.projectIamAdmin

---

Architecture

The production deployment creates a fully managed, highly available infrastructure:

---

Phase 1: Infrastructure Setup (30 minutes)

Step 1: Create Terraform State Backend

Quick Setup

cd terraform/backend-setup-gcp

# Create terraform.tfvars
cat > terraform.tfvars <<EOF
project_id    = "YOUR_PROJECT_ID"
region        = "us-central1"
bucket_prefix = "mcp-langgraph"
EOF

# Deploy
terraform init
terraform apply -auto-approve

# Save bucket name
export TF_STATE_BUCKET=$(terraform output -raw terraform_state_bucket)

Expected: GCS bucket created with versioning and logging enabled

Step 2: Configure Production Environment

cd terraform/environments/gcp-prod

## Update main.tf with state bucket
sed -i "s/bucket = \"mcp-langgraph-terraform-state-us-central1-XXXXX\"/bucket = \"$TF_STATE_BUCKET\"/g" main.tf

## Create terraform.tfvars
cp terraform.tfvars.example terraform.tfvars

Edit terraform.tfvars with your configuration:

project_id  = "YOUR_PROJECT_ID"
region      = "us-central1"
team        = "platform"
cost_center = "engineering"

## Security (enable after Phase 4)
enable_binary_authorization = false
enable_private_endpoint     = false  # Set true for maximum security

## Optional: Restrict control plane access
master_authorized_networks_cidrs = [
  {
    cidr_block   = "YOUR_IP/32"
    display_name = "My IP"
  }
]

Important: Replace YOUR_PROJECT_ID with your actual GCP project ID throughout the configuration.

---

Phase 2: Deploy Infrastructure (25 minutes)

Step 1: Initialize and Plan

cd terraform/environments/gcp-prod
terraform init
terraform plan -out=tfplan

Review the plan. It should create:

• 1 VPC network with 3 subnets
• 1 GKE Autopilot cluster (regional)
• 1 Cloud SQL instance (PostgreSQL 15, HA)
• 1 Memorystore instance (Redis 7.0, HA)
• 2 NAT IPs
• Multiple service accounts
• IAM bindings
• Firewall rules
• Monitoring alerts

Step 2: Deploy

terraform apply tfplan

Duration: 20-25 minutes. Cloud SQL takes 10-12 minutes, GKE takes 8-10 minutes.

Step 3: Configure kubectl

## Get credentials
eval $(terraform output -raw kubectl_config_command)

## Verify access
kubectl cluster-info
kubectl get nodes
kubectl get namespaces

Expected output:

Kubernetes control plane is running at https://X.X.X.X
NAME              STATUS   AGE
default           Active   5m
kube-system       Active   5m

---

Phase 3: Application Deployment (20 minutes)

Step 1: Create Secrets in Secret Manager

PROJECT_ID="YOUR_PROJECT_ID"

## Create secret
gcloud secrets create mcp-production-secrets \
  --replication-policy="automatic" \
  --project="$PROJECT_ID"

## Prepare secret data
cat > /tmp/secrets.json <<EOF
{
  "anthropic_api_key": "sk-ant-...",
  "google_api_key": "AIza...",
  "jwt_secret": "$(openssl rand -base64 32)",
  "postgres_password": "$(terraform output -raw cloudsql_user_password)",
  "cloudsql_connection_name": "$(terraform output -raw cloudsql_connection_name)",
  "redis_host": "$(terraform output -raw redis_host)",
  "redis_port": "$(terraform output -raw redis_port)",
  "redis_password": "$(terraform output -raw redis_auth_string)"
}
EOF

## Upload secrets
gcloud secrets versions add mcp-production-secrets \
  --data-file=/tmp/secrets.json \
  --project="$PROJECT_ID"

## Cleanup
rm -f /tmp/secrets.json

Step 2: Install External Secrets Operator

helm repo add external-secrets https://charts.external-secrets.io
helm repo update

helm install external-secrets \
  external-secrets/external-secrets \
  --namespace external-secrets-system \
  --create-namespace \
  --set installCRDs=true

kubectl wait --for=condition=ready pod \
  -l app.kubernetes.io/name=external-secrets \
  -n external-secrets-system \
  --timeout=120s

Step 3: Deploy Application

cd deployments/overlays/production-gke

## Update with your project ID
sed -i "s/PROJECT_ID/$PROJECT_ID/g" *.yaml

## Deploy
kubectl apply -k .

## Watch rollout
kubectl rollout status deployment/production-mcp-server-langgraph \
  -n mcp-production \
  --timeout=10m

Verify deployment:

kubectl get pods -n mcp-production
kubectl logs -n mcp-production -l app=mcp-server-langgraph --tail=50

---

Phase 4: Security Hardening (30 minutes)

Binary Authorization

Enable image signing to ensure only trusted container images run in your cluster:

1. Run Setup Script

   ./deployments/security/binary-authorization/setup-binary-auth.sh \
     PROJECT_ID \
     production
   

   This creates:

   • KMS key for signing
   • Binary Authorization attestor
   • Enforcement policy
2. Sign Images

   IMAGE="us-central1-docker.pkg.dev/PROJECT_ID/mcp-production/mcp-server-langgraph:2.8.0"
   
   ./deployments/security/binary-authorization/sign-image.sh \
     PROJECT_ID \
     production \
     "$IMAGE"
   

3. Enable in Cluster

   Edit terraform.tfvars:

   enable_binary_authorization = true
   

   Apply:

   terraform apply -auto-approve
   

Learn more about Binary Authorization for complete setup details.

---

Phase 5: Observability (10 minutes)

Setup Monitoring

## Create dashboards and alerts
./monitoring/gcp/setup-monitoring.sh PROJECT_ID

This configures:

• Custom Cloud Monitoring dashboard
• Alert policies (CPU, memory, errors, latency)
• Uptime checks
• SLO definitions

Access Dashboards

GKE Workloads

View pod status, deployments, services

Cloud Monitoring

Custom dashboards, metrics, alerts

Cloud Logging

Centralized log aggregation

Cloud Trace

Distributed tracing

---

Verification & Testing

Health Checks

Test all health endpoints:

Port Forward

kubectl port-forward -n mcp-production \
  svc/production-mcp-server-langgraph 8000:8000 &

curl http://localhost:8000/health/live
curl http://localhost:8000/health/ready
curl http://localhost:8000/health/startup

All health checks should return HTTP 200

Database Connectivity

kubectl exec -it -n mcp-production \
  $(kubectl get pod -n mcp-production -l app=mcp-server-langgraph -o jsonpath='{.items[0].metadata.name}') \
  -c cloud-sql-proxy \
  -- wget -qO- http://localhost:9801/readiness

Should return: ok

Workload Identity

Verify service account annotation:

kubectl get sa production-mcp-server-langgraph \
  -n mcp-production \
  -o jsonpath='{.metadata.annotations.iam\.gke\.io/gcp-service-account}'

Should show: mcp-prod-app-sa@PROJECT_ID.iam.gserviceaccount.com

---

Troubleshooting

Pods Stuck in Pending

Diagnosis:

kubectl describe pod POD_NAME -n mcp-production

Common causes:

1. Resource requests too high (Autopilot provisions automatically but has limits)
2. Image pull errors (check Workload Identity permissions)
3. Binary Authorization blocking unsigned images

Solution:

• Reduce CPU/memory requests in deployment
• Verify image exists in Artifact Registry
• Sign the image if Binary Auth is enabled

Can't Access Cloud SQL

Diagnosis:

kubectl logs -n mcp-production POD_NAME -c cloud-sql-proxy

Solution:

1. Verify private service connection exists
2. Check Cloud SQL instance is running
3. Verify Cloud SQL Proxy sidecar configuration
4. Check Workload Identity IAM bindings

Workload Identity Not Working

Diagnosis:

kubectl get sa -n mcp-production production-mcp-server-langgraph -o yaml

gcloud iam service-accounts get-iam-policy \
  mcp-prod-app-sa@PROJECT_ID.iam.gserviceaccount.com

Solution:

1. Verify annotation: iam.gke.io/gcp-service-account
2. Check IAM binding exists
3. Wait 1-2 minutes for propagation

For complete troubleshooting, see GKE Operational Runbooks.

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Cost Optimization

Expected Monthly Costs

Production

Component	Configuration	Cost/Month
GKE Autopilot	~25 pods (500m CPU, 1GB RAM avg)	$360
Cloud SQL	4 vCPU, 15GB RAM, HA + replica	$280
Memorystore	5GB Redis HA	$220
Networking	NAT, egress	$60
Monitoring	Standard retention	$50
Total		$970/month

vs. Traditional GKE: $1,290/month (Save$320/month = 25%)

Cost Optimization Guide

Learn how to achieve 40-60% cost savings with rightsizing, committed use discounts, and automation.

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Next Steps

Operational Runbooks

Day-2 operations, incident response, maintenance procedures

Security Hardening

Enable VPC Service Controls, configure Cloud Armor, implement policies

CI/CD Pipeline

Setup automated deployments with ArgoCD and GitHub Actions

Monitoring & SLOs

Configure custom dashboards, define SLIs/SLOs, set up alerts

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Related Documentation

Infrastructure as Code

Terraform modules for VPC, GKE, Cloud SQL, Redis

Multi-Environment Setup

Dev, staging, production configurations

Disaster Recovery

Multi-region failover and backup automation

Service Mesh

Anthos Service Mesh for advanced traffic management

Binary Authorization

Image signing and policy enforcement

GKE Staging

Staging environment setup

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Support Resources

Complete Technical Documentation

For detailed technical documentation, see:

• GKE Deployment Guide (800+ lines, root directory)
• Terraform Module READMEs (5,000+ lines technical docs)
• GCP Best Practices Summary (root directory)

Need help? Check our operational runbooks.