Aracli Deploy Management

# Deploying OpenClaw Agent Systems

> Skill by [ara.so](https://ara.so) — Daily 2026 Skills collection.

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.

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## Infrastructure Options

### 1. Cloud VMs (AWS, GCP, Azure, Hetzner)

Spin up VMs and run agents as containerized services.

```bash
# Example: Docker Compose on a cloud VM
docker compose up -d agent-runtime
```

**Pros:**
- Familiar ops tooling (Terraform, Ansible, etc.)
- Easy to scale horizontally — just add more VMs
- Pay-as-you-go pricing on most providers
- Full control over networking and security

**Cons:**
- You own the uptime — no managed restarts or healing
- GPU instances get expensive fast
- Cold start if you're spinning up on demand

**Best for:** Teams that already have cloud infrastructure and want full control.

---

### 2. Managed Container Platforms (Railway, Fly.io, Render)

Deploy agent containers without managing VMs directly.

```bash
# Example: Railway
railway up

# Example: Fly.io
fly deploy
```

**Pros:**
- Zero server management — just push code
- Built-in health checks, auto-restarts, and scaling
- Easy preview environments for testing agent changes
- Usually includes logging and metrics out of the box

**Cons:**
- Less control over the underlying machine
- Can get costly at scale compared to raw VMs
- Cold starts on free/hobby tiers
- GPU support is limited or nonexistent on most platforms

**Best for:** Small teams that want to move fast without an ops burden.

---

### 3. Bare Metal (Hetzner Dedicated, OVH, Colo)

Run agents directly on physical servers for maximum performance per dollar.

```bash
# Example: systemd service on bare metal
sudo systemctl start agent-runtime
```

**Pros:**
- Best price-to-performance ratio, especially for GPU workloads
- No noisy neighbors — predictable latency
- Full control over hardware, kernel, drivers
- No egress fees

**Cons:**
- You manage everything: OS, networking, failover, monitoring
- Scaling means ordering and provisioning new hardware
- No managed load balancing — you build it yourself

**Best for:** Cost-sensitive workloads, GPU-heavy inference, or teams with strong ops skills.

---

### 4. Serverless / Edge (Lambda, Cloudflare Workers, Vercel Functions)

Run lightweight agent logic at the edge without persistent infrastructure.

```bash
# Example: deploy to Cloudflare Workers
wrangler deploy
```

**Pros:**
- Zero idle cost — pay only for invocations
- Global distribution with low latency
- No servers to patch or maintain
- Scales to zero and back automatically

**Cons:**
- Execution time limits (often 30s–300s)
- No persistent state between invocations
- Not suitable for long-running agent sessions
- Limited runtime environments (no arbitrary binaries)

**Best for:** Stateless agent endpoints, webhooks, or lightweight tool-calling proxies.

---

### 5. Hybrid

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:**
- Each layer runs on the most cost-effective infra
- API layer gets managed scaling, agent layer gets raw performance
- Can migrate layers independently

**Cons:**
- More moving parts to coordinate
- Cross-network latency between layers
- Multiple deployment pipelines to maintain

**Best for:** Production systems that need both cheap inference and a polished API layer.

---

## Management Methods: CLI vs