Deploy AI Models on Railway

Railway provides simple, developer-friendly infrastructure for deploying containerized AI models.

Prerequisites

• Railway account
• Railway CLI installed
• Docker knowledge
• GitHub repository (optional)

Deployment Methods

1. From GitHub

Connect your repository and Railway auto-deploys on push:

# Install Railway CLI
npm install -g @railway/cli

# Login
railway login

# Link project
railway link

# Deploy
railway up

2. From Dockerfile

Create a Dockerfile:

FROM python:3.10

WORKDIR /app

COPY requirements.txt .
RUN pip install -r requirements.txt

COPY . .

CMD ["python", "app.py"]

Railway automatically detects and builds it.

3. Using Templates

Railway offers pre-built templates for common AI frameworks.

Configuration

railway.json

{
  "build": {
    "builder": "DOCKERFILE",
    "dockerfilePath": "Dockerfile"
  },
  "deploy": {
    "startCommand": "python app.py",
    "restartPolicyType": "ON_FAILURE",
    "restartPolicyMaxRetries": 10
  }
}

Environment Variables

Add via Railway Dashboard or CLI:

railway variables set HUGGING_FACE_TOKEN=your-token

Persistent Storage

Railway provides volumes for persistent data:

railway volume create model-cache
railway volume attach model-cache /app/cache

Monitoring

• View logs in real-time
• Monitor resource usage
• Set up health checks
• Configure alerts

Cost Optimization

• Use appropriate resource limits
• Implement auto-sleep for dev environments
• Monitor bandwidth usage
• Optimize Docker image size

Production Checklist

• [ ] Set up custom domain
• [ ] Configure environment variables
• [ ] Add persistent volumes
• [ ] Set up health checks
• [ ] Configure resource limits
• [ ] Enable monitoring
• [ ] Set up backups
• [ ] Document deployment process