How to Deploy Jan on a VPS

copyapt update && apt upgrade -y
adduser deploy
usermod -aG sudo deploy

copyufw allow OpenSSH
ufw allow 80
ufw allow 443
ufw enable

copycurl -fsSL https://get.docker.com | sh
usermod -aG docker deploy

#Install Jan

Create a working directory:

copymkdir ~/jan && cd ~/jan

Create a compose file:

copyservices:
  jan:
    image: janhq/jan-server:latest
    restart: unless-stopped
    ports:
      - "127.0.0.1:1337:1337"
    volumes:
      - jan_data:/app/data

volumes:
  jan_data:

Start it:

copydocker compose up -d

Jan listens on port 1337. The 127.0.0.1 bind keeps it private.

#Connect a model backend

Jan needs a model backend to generate responses. You have two options:

Option 1: Ollama (recommended) If Ollama is on the same server, Jan can reach it at http://host.docker.internal:11434. This is the simplest path — Ollama handles model management, and Jan provides the interface.

Option 2: Local models Jan can download and run models directly using its built-in engine. This requires more RAM (the model weights load into Jan's process) and gives you more control over model selection.

To configure the backend:

1. Load the Jan web interface
2. Go to Settings → Model Providers
3. Add Ollama (or another provider)
4. Enter the connection details

If you're using Ollama, make sure at least one model is pulled:

copydocker exec -it ollama ollama pull llama3.2

#HTTPS + domain

Jan needs HTTPS for production use, especially if you're accessing it remotely. Point a reverse proxy at 127.0.0.1:1337 and terminate HTTPS on 443.

The simplest path is Automatic HTTPS with Caddy. Point an A record for your hostname (say ai.example.com) at the server's public IP, then have Caddy reverse-proxy that hostname to 127.0.0.1:1337.

If you're using the Caddy container approach, put Jan and Caddy in the same compose file and proxy to the Jan service name:

reverse_proxy jan:1337

#First-run setup

Load https://ai.example.com in your browser. Jan shows a clean, minimal interface.

Set up your account:

1. Create an admin account (first account becomes admin)
2. Configure model providers (Settings → Model Providers)
3. Select a default model for new conversations

Test the connection:

1. Start a new conversation
2. Type a test message
3. Confirm you get a response from your configured backend

If the response fails, check the Jan logs:

copydocker compose logs jan | tail -20

Common issues: model provider not reachable, wrong URL, or Ollama not running.

#Model management

Jan's model management depends on your backend:

With Ollama: Jan shows models from your Ollama instance. Pull models in Ollama, and they appear in Jan's model dropdown. This is the recommended approach for server deployments.

With Jan's built-in engine: Jan can download models directly. Go to Settings → Models → Download Model and select from the available options. Models are stored in the Jan data volume.

Hybrid approach: Use Ollama for the heavy models (7B+) and Jan's built-in engine for smaller, faster models (3B) for quick questions.

#Backups

Jan's data lives in the data volume:

copydocker run --rm -v jan_data:/data -v $(pwd):/backup alpine \
  tar czf /backup/jan-$(date +%F).tar.gz -C /data .

This backs up conversations, settings, and downloaded models (if using Jan's built-in engine). For Ollama, see Deploy Ollama on a VPS for separate backup instructions.

#Upgrades

Pull the newer image and recreate:

copydocker compose pull
docker compose up -d

Jan updates are usually non-breaking. Check the Jan changelog for any notable changes.

#Troubleshooting

Can't connect to Ollama. Verify Ollama is running (docker compose ps in the Ollama directory) and the URL is correct. If both are on the same server, http://host.docker.internal:11434 should work.

Slow responses. Expected without a GPU. Jan's built-in engine on CPU is slower than Ollama for the same models. Consider using Ollama as the backend for better performance.

Model not found. The model name must match exactly what's available in your backend. Check Ollama's model list with curl http://localhost:11434/api/tags.

Web interface won't load. Check that Jan is running (docker compose ps) and the port is correct. Look at logs for startup errors.

Conversations missing after restart. Jan stores data in the Docker volume. If the volume was removed or recreated, conversations are lost. Back up the data volume regularly.

#Verification + next steps

You're done when you can: load the web interface over HTTPS, start a conversation, get responses from your configured backend, and switch between models. The interface should feel responsive and the conversations should persist.

From here, explore Jan's features like conversation templates, model fine-tuning, and keyboard shortcuts. For a more feature-rich web interface, see Open WebUI. For document-based AI workflows, see AnythingLLM. A 2 vCPU / 4 GB box handles remote-API deployments; step up to 8 vCPU / 16 GB for local models. See Best VPS for AI & ML Workloads for the ranked picks.