How to Deploy a Python App
Deploying a Python app means moving it from your local machine to a host where it can receive traffic. The right method depends on your app's complexity, expected load, and how much control you want over the environment.
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Choose a Deployment Target
Start by matching your app to a target that fits its needs. The table below compares common options.
| Target | Best For | Control Level | Complexity |
|---|---|---|---|
| Platform-as-a-Service (Heroku, Render, Railway) | Small to medium apps, fast time-to-market | Low | Low |
| Container Registry + Cloud Run / ECS | Microservices, reproducible builds | Medium | Medium |
| VPS or Bare Server (DigitalOcean, Hetzner) | Full control, custom stacks | High | High |
| Serverless (AWS Lambda, GCP Functions) | Event-driven, lightweight APIs | Low | Medium |
Prepare the App for Deployment
Before moving code, pin your dependencies and runtime. Use a requirements.txt or pyproject.toml with exact versions, set PYTHONUNBUFFERED=1 for logs, and make sure your app listens on the host and port the platform expects (often 0.0.0.0 and $PORT).
Set Up a Production WSGI or ASGI Server
Never rely on the development server. Use Gunicorn for WSGI apps or Uvicorn for ASGI apps. A typical Gunicorn command looks like gunicorn -w 4 -b 0.0.0.0:$PORT app:app, where the numbers and worker count should be tuned to your workload.
Configure the Environment and Secrets
Store sensitive values in environment variables or a secrets manager, not in code. Create a .env.example for local development and set production variables in your host's dashboard or CI pipeline.
Automate the Deploy
Use a CI/CD tool to run tests, build artifacts, and push to your host on every merge. A simple flow might be: lint and test on push, build a Docker image, push to a registry, then trigger a deploy. Keep the deploy script idempotent so it can run safely many times.
After Deploy: Health Checks and Monitoring
Add a lightweight health endpoint and set up log collection and error tracking. Watch startup time, memory usage, and request latency to catch regressions early.