AI-Powered Deployment

AI Deploy uses a language model to analyze a project and generate a Docker Compose configuration automatically. Paste a URL, review the generated config, and deploy.

Prerequisites

AI Deploy requires an AI provider to be configured in Settings > AI Provider:

Any OpenAI-compatible API endpoint works.

How It Works

Step 1: Provide a URL

Navigate to Deploy > AI Deploy and enter:

Compose-repo hint (AI-299)

When the typed URL points at a public GitHub repo whose root already contains a docker-compose.yml (or .yaml / compose.yml / .yaml), an info banner appears above the Analyze button:

Docker Compose detected — This repo ships its own docker-compose.yml. AI Deploy is designed for single-Dockerfile repos. For multi-service compose layouts, the Docker Compose deploy path uses your file directly and saves the AI analysis cost. [Switch to Compose Deploy]

Click the switch link to jump straight to Deploy > Docker Compose with your customer assignment preserved. AI Deploy will still let you proceed if you want to try the analyzer-generated config — the banner is informational, not a block.

The check runs only for public repos (it uses GitHub's unauthenticated Contents API). For private repos, rate-limited IPs, or non-GitHub URLs no banner appears — the analyzer will still run and may surface the compose during its scan.

Step 2: AI Analysis

The panel sends the URL to the configured AI model, which:

1. Fetches and reads the repository README, Dockerfile, compose files, and package manifests
2. Identifies the tech stack, dependencies, and required services
3. Generates a Docker Compose configuration with:
   • Correct base images and versions
   • Required environment variables with sensible defaults
   • Volume mounts for persistent data
   • Health check endpoints
   • Multi-service setups (e.g., app + database)
4. Returns the configuration with an explanation of design decisions

Analysis progress streams to the UI via WebSocket so you can watch the AI's reasoning.

Step 3: Review and Edit

The generated Docker Compose configuration is displayed in an editor. You can:

• Review the AI's explanation of why it chose specific images and settings
• Modify environment variables, image versions, or resource limits
• Add or remove services
• Adjust volume mounts or networking

The editor supports YAML syntax highlighting and validation.

Step 4: Deploy

Click Deploy to build and run the repository through the git build pipeline (the same pipeline as Git Deploy):

1. The repository is cloned and built into an image (Dockerfile if the analyzer detected one, auto-detected buildpacks otherwise)
2. The analyzer's detected application port becomes the Traefik routing target and is injected as the PORT environment variable (Heroku/Render convention)
3. Containers are started, health checks are monitored
4. SSL certificates are provisioned automatically

The compose configuration shown on the Review step documents what the analyzer found — the deploy itself builds from source, so repositories without any prebuilt image work out of the box.

Use Cases

AI Deploy works well for:

• Unknown projects — paste a GitHub URL and let AI figure out how to run it
• Quick prototyping — get a service running without reading deployment docs
• Complex stacks — AI handles multi-service setups (app, database, cache, worker)
• Documentation links — paste a project's install docs and AI extracts the deployment steps

Limitations

• AI-generated configurations should always be reviewed before deploying
• Quality depends on the AI model — larger models produce better results
• Private repositories require the Git Deploy method instead
• Very new or niche projects may not be well-known to the AI model
• The AI does not have access to the repository's actual source code beyond what is publicly readable

Fallback

If the AI-generated configuration does not work:

1. Check the container logs for errors
2. Edit the generated compose file in the service settings
3. Use Git Deploy or Compose Deploy for more control