In This Article
Introduction
Google Cloud Platform offers robust infrastructure for OpenClaw. Compute Engine provides simple VM hosting. GKE runs containerized agents at scale. Vertex AI can supply Gemini models if you prefer Google's ecosystem. Here's what we're covering: GCP deployment: step-by-step GCE setup, GKE configuration, Vertex AI integration, and cost optimization.
Whether you're a Google-centric organization or choosing GCP for specific regions (e.g., australia-southeast1), you'll find actionable steps. We'll cover instance sizing, Secret Manager, and the patterns that make OpenClaw run reliably on GCP.
Compute Engine: Step-by-Step
Create a VM (e2-medium or larger). Install Docker, run OpenClaw. Use a static IP for stability. Persistent disks for memory and config. Consider preemptible VMs for cost savings if acceptable.
Step 1: Create VM. gcloud compute instances create openclaw --zone=us-central1-a --machine-type=e2-medium --image-family=ubuntu-2204-lts --image-project=ubuntu-os-cloud. Or use Console.
Step 2: Instance sizing. e2-medium (2 vCPU, 4GB): ~$25/month. e2-standard-2 (2 vCPU, 8GB): ~$50/month. For Ollama: e2-standard-2 or larger. Cloud LLM only: e2-medium suffices.
Step 3: Persistent disk. 30GB standard or balanced. Attach to VM. Mount for /app/config. Survives VM recreation if disk is separate.
Step 4: Static IP. Reserve static IP. Assign to VM. Prevents IP change on stop/start.
Step 5: Firewall. Create rule: allow tcp:22 from your IP. allow tcp:3000 if web UI. allow egress for 443 (API calls).
Step 6: Deploy. SSH in. Install Docker. docker run ... Same as other platforms. Use startup script for automation.
Preemptible. 60–80% cheaper. Can be terminated with 30s notice. OpenClaw persists to disk — restart on new preemptible. Good for dev/test. Not for production-critical.
Kubernetes (GKE)
GKE runs OpenClaw as a Deployment. Scale replicas for multiple agents. Use ConfigMaps for config, Secrets for API keys. Ingress for web interfaces. GKE Autopilot simplifies node management.
Deployment. apiVersion: apps/v1, kind: Deployment, spec: replicas: 1, template: spec: containers: [- name: openclaw, image: openclaw/openclaw, envFrom: [secretRef: name: openclaw-secrets], volumeMounts: [- name: config, mountPath: /app/config], volumes: [- name: config, persistentVolumeClaim: claimName: openclaw-config].
Secrets. kubectl create secret generic openclaw-secrets --from-literal=OPENAI_API_KEY=sk-... Or use Secret Manager with Workload Identity.
ConfigMaps. Store config.yaml in ConfigMap. Mount as file. Or use GCS bucket + init container to pull config.
Autopilot. No node management. Pay per pod. Simpler. Slightly higher cost than standard. Good for getting started.
Vertex AI Integration
Vertex AI provides Gemini and other models via API. OpenClaw can use Vertex as an LLM provider. Keeps everything in GCP for enterprises committed to Google's cloud. Check OpenClaw docs for Vertex provider configuration.
Benefits. Data stays in GCP. Enterprise SLAs. Gemini models. No OpenAI/Anthropic dependency. Good for Google-centric orgs.
Setup. Enable Vertex AI API. Create service account with Vertex AI User role. Use application default credentials or service account key. Configure OpenClaw: provider: vertex, model: gemini-1.5-pro (or gemini-1.5-flash for cost).
Pricing. Gemini 1.5 Flash: ~$0.075/1M input tokens. Gemini 1.5 Pro: ~$1.25/1M input. Compare to OpenAI. Often competitive.
Region Selection
us-central1 (Iowa), europe-west1 (Belgium), asia-southeast1 (Singapore), australia-southeast1 (Sydney). Choose based on data residency and latency to your users.
Latency. us-central1 to Vertex AI: excellent. Match region to your users. australia-southeast1 for AU data residency.
Cost Optimization
Preemptible for dev. Committed use discounts for production (1-year, 3-year). Right-size. e2-medium is often sufficient. Monitor API costs — Vertex or external.
Real numbers. e2-medium: ~$25/month. 30GB disk: ~$5/month. Total infra: ~$30/month. API: $30–100/month. Total: $60–130/month.
Implementation Checklist
- □ Choose region for data residency
- □ Create GCE VM or GKE cluster
- □ Configure firewall. Minimal access
- □ Store secrets in Secret Manager
- □ Deploy with Docker/K8s. Mount config
- □ Reserve static IP
- □ Set up Cloud Logging
Common Pitfalls to Avoid
Pitfall 1: Default region. us-central1 is default. If you need EU, use europe-west1. Check before create.
Pitfall 2: Preemptible for production. Don't. Use for dev only. Production needs standard VMs.
Pitfall 3: No persistence in GKE. Use PersistentVolume for config. Ephemeral storage is lost on pod restart.
Frequently Asked Questions
Does OpenClaw support Vertex AI natively? Check current OpenClaw docs. Vertex integration may be via custom provider or community Skill. Gemini models are well-supported.
Can I use Cloud Run? Cloud Run is for request-response workloads. OpenClaw is long-running (Heartbeat, message listening). Not a natural fit. Use GCE or GKE.
What about GCP Free Tier? e2-micro is too small. e2-small might work for very light use. Expect limitations. e2-medium is minimum recommended.
How do I backup config on GCE? Snapshot the disk. Or: gsutil -m cp -r ./config gs://your-bucket/openclaw-backup/.
Can I use Workload Identity with Vertex? Yes. GKE pod with Workload Identity can access Vertex without service account keys. More secure.
Wrapping Up
GCP is a solid choice for OpenClaw, especially for Google-centric organizations. GCE for simplicity, GKE for scale, Vertex for integrated AI. OpenClaw Consult advises on GCP architecture — we've deployed for enterprises using GCP as primary cloud.