Deploy an OpenClaw AI Agent on VectorLay in Minutes

What is OpenClaw?

OpenClaw (sometimes called ClawdBot by the community) is an open-source AI agent that runs headless as a Node.js daemon. Unlike cloud-hosted chatbot services, OpenClaw runs entirely on your own infrastructure. You connect it to the messaging apps you already use — Signal, Telegram, WhatsApp, Discord — and interact with it just like texting a friend.

It can browse the web with its built-in Chromium instance, execute code, search the internet, manage files, and chain together complex multi-step tasks. Think of it as a personal AI assistant that lives on a server you control.

Why Run It on VectorLay?

You could spin up a VPS somewhere and spend an afternoon installing Node.js, Chromium, configuring drivers, and debugging dependency issues. Or you could deploy on VectorLay in about two minutes.

Every OpenClaw VM on VectorLay is a dedicated, isolated virtual machine — not a shared container. Your agent runs inside its own secure enclave with its own kernel, filesystem, and network stack. No noisy neighbors, no shared resources, no data leaking between tenants. This matters when your agent is processing private conversations and has access to your messaging accounts.

CPU vs GPU: Which One Do You Need?

We offer two OpenClaw templates. Pick the one that matches your use case:

Most users should start with CPU. If you're pointing OpenClaw at an external API like Claude or GPT, you don't need a GPU — you just need a reliable VM that stays online. The CPU template is cheaper and perfectly capable of running the agent, Chromium, and any background tasks.

Choose the GPU template if you want to run a local model (like Llama, Mistral, or Qwen) directly on the node. The GPU VM comes with NVIDIA 575.x drivers and the NVIDIA Container Toolkit, so you can spin up an inference server in Docker alongside your agent.

Step 1: Create a VM

Log in to the VectorLay dashboard and create a new VM:

1. Click VMs in the sidebar
2. Click New VM
3. Select the Ubuntu 24.04 + OpenClaw template (or the NVIDIA 575 variant for GPU)
4. Choose your node size — for CPU, even a small node works fine
5. Click Create

Your VM boots in under a minute. Once it's running, you'll see an SSH command on the VM detail page.

Step 2: Configure OpenClaw

SSH into your VM and set up OpenClaw:

# SSH into your VM (command from dashboard)
ssh ubuntu@your-vm.run.vectorlay.com

# Initialize OpenClaw config (give your agent a name)
openclaw init --name clawdbot

# Edit the config to add your API keys and messaging bridges
nano ~/.openclaw/config.yaml

The config file is where you connect OpenClaw to your messaging platforms and your AI provider of choice.

Step 3: Connect Your Messaging Apps

OpenClaw supports multiple messaging bridges. You'll need to configure at least one to start interacting with your agent:

Step 4: Start the Agent

Once your config is set, start OpenClaw:

# Start OpenClaw as a background daemon
openclaw start --daemon

# Check the status
openclaw status

# View logs
openclaw logs --follow

That's it. Send a message to your agent on Signal, Telegram, or whichever platform you configured, and it'll respond.

Running a Local Model (GPU Template)

If you chose the GPU template, you can run a local model instead of relying on external APIs. This keeps everything on your VM — your conversations, your model weights, your data. Nothing leaves the machine.

# Pull and run a local model with vLLM
docker run -d --gpus all \
  -p 8000:8000 \
  vllm/vllm-openai:latest \
  --model mistralai/Mistral-7B-Instruct-v0.3

# Point OpenClaw at your local model
# In ~/.openclaw/config.yaml:
#   provider: openai-compatible
#   base_url: http://localhost:8000/v1
#   model: mistralai/Mistral-7B-Instruct-v0.3

With GPU acceleration, the local model responds in milliseconds. And since both the agent and the model run inside the same isolated VM, there's zero network latency between them.

Security and Isolation

Every VM on VectorLay runs in its own hardware-isolated enclave. This isn't container-level isolation — it's full VM isolation with QEMU/KVM and VFIO GPU passthrough:

• Dedicated kernel: Your VM runs its own Linux kernel, completely separate from the host and other VMs
• Memory isolation: Hardware-enforced memory boundaries — no Spectre/Meltdown cross-VM leaks
• Network isolation: Your own network stack with a private IP, behind a WireGuard tunnel
• GPU passthrough: On GPU VMs, the entire GPU is passed through via VFIO — not shared, not virtualized
• Encrypted transit: All traffic between your VM and the internet goes through WireGuard encryption

When your agent is handling private messages and has credentials to your messaging accounts, this level of isolation isn't optional — it's essential.

Pricing

VectorLay is pay-as-you-go. You're billed hourly for the time your VM is running. No commitments, no contracts — shut it down when you don't need it, start it back up when you do.

• CPU template: Starts at a few cents per hour. Perfect for agents using external APIs.
• GPU template: Varies by GPU type. An RTX 4090 node runs a 7B model comfortably and costs a fraction of what you'd pay on AWS or GCP.

Check the pricing page for current rates.

Wrapping Up

Running your own AI agent doesn't have to be complicated. With VectorLay's OpenClaw templates, you get a ready-to-go VM with everything pre-installed. Pick CPU or GPU, connect your messaging apps, and you're live.

• Pre-installed Node.js 22, Chromium, and OpenClaw
• Hardware-isolated VM — your own secure enclave
• GPU option for running local models privately
• Pay-as-you-go, billed hourly
• Connect to Signal, Telegram, WhatsApp, Discord

Questions? Join our Discord or check the docs.