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Guides deploying and managing OpenClaw AI agent systems on cloud VMs (AWS/GCP/Azure), managed platforms (Railway/Fly.io), bare metal (Hetzner/OVH), and serverless (Vercel/Cloudflare). Compares CLI/API/MCP management.
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A practical guide to deploying and managing OpenClaw-compatible AI agent systems. Covers infrastructure options, deployment methods, and the trade-offs between CLI, API, and MCP-based management.
Spin up VMs and run agents as containerized services.
# Example: Docker Compose on a cloud VM
docker compose up -d agent-runtime
Pros:
Cons:
Best for: Teams that already have cloud infrastructure and want full control.
Deploy agent containers without managing VMs directly.
# Example: Railway
railway up
# Example: Fly.io
fly deploy
Pros:
Cons:
Best for: Small teams that want to move fast without an ops burden.
Run agents directly on physical servers for maximum performance per dollar.
# Example: systemd service on bare metal
sudo systemctl start agent-runtime
Pros:
Cons:
Best for: Cost-sensitive workloads, GPU-heavy inference, or teams with strong ops skills.
Run lightweight agent logic at the edge without persistent infrastructure.
# Example: deploy to Cloudflare Workers
wrangler deploy
Pros:
Cons:
Best for: Stateless agent endpoints, webhooks, or lightweight tool-calling proxies.
Combine approaches: use managed platforms for the API layer and bare metal for the agent runtime.
User → API (Railway/Vercel) → Agent Runtime (bare metal GPU)
Pros:
Cons:
Best for: Production systems that need both cheap inference and a polished API layer.
Once your agents are deployed, you need a way to manage them — ship updates, check status, roll back. There are three main approaches.
A command-line tool that talks to your agent infrastructure over SSH or HTTP.
# Typical CLI workflow
mycli status
mycli deploy --service agent
mycli rollback
mycli logs agent --tail
Pros:
Cons:
Best for: Day-to-day operations by the team that built the system.
A REST or gRPC API that exposes deployment operations programmatically.
# Deploy via API
curl -X POST https://deploy.example.com/api/v1/deploy \
-H "Authorization: Bearer $TOKEN" \
-d '{"service": "agent", "version": "v42"}'
# Check status
curl https://deploy.example.com/api/v1/status
Pros:
Cons:
Best for: Teams building internal platforms or integrating deploys into larger systems.
Expose deployment operations as MCP tools so AI agents can manage infrastructure directly.
{
"tool": "deploy",
"input": {
"service": "agent",
"version": "latest",
"strategy": "rolling"
}
}
Pros:
Cons:
Best for: Agentic DevOps workflows where AI agents participate in the deploy lifecycle.
| CLI | API | MCP | |
|---|---|---|---|
| Speed to set up | Fast | Medium | Medium |
| Automation | Scripts/CI | Any HTTP client | Agent-native |
| Audience | Engineers | Engineers + systems | Engineers + agents |
| Observability | Terminal output | Structured responses | Tool call logs |
| Auth model | SSH keys / tokens | API tokens / OAuth | MCP auth scopes |
| Best paired with | Bare metal, VMs | Managed platforms | Agent orchestrators |