UPDATE: What’s the difference between Flask and FastAPI for model deployment? (#28)

Co-authored-by: FAQ Bot <[email protected]>

Author: github-actions[bot]

_questions/machine-learning-zoomcamp/module-10/018_23f5be95ef_python-fastapi-deployment.md

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 id: 23f5be95ef
-question: Which language and framework are used for deployment?
+question: What’s the difference between Flask and FastAPI for model deployment?
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-Python with FastAPI (updated from Flask). You’ll also package/serve models, containerize, and deploy to cloud/Kubernetes.
+FastAPI vs Flask for ML model deployment
+
+- Performance: FastAPI is built on ASGI and supports asynchronous endpoints, which can yield higher throughput and lower latency for ML API workloads. Flask runs on WSGI and is synchronous by default, which can be a bottleneck under high concurrent load.
+- Type hints & validation: FastAPI uses Python type hints and Pydantic models to automatically validate and serialize requests and responses, reducing boilerplate and minimizing invalid input. Flask relies on manual validation or external libraries/extensions.
+- Auto documentation: FastAPI auto-generates API docs (Swagger UI and ReDoc) from your code and type hints. Flask typically requires external extensions to provide similar documentation.
+- Development speed: Built-in validation, dependency injection, and async support in FastAPI reduce boilerplate and accelerate development for ML APIs.
+- Community & maturity: Flask has a larger, established ecosystem. FastAPI is newer but growing quickly and is popular for modern ML API deployments.
+
+Deployment context: Both frameworks can be containerized and deployed to cloud providers or Kubernetes. For higher-concurrency and modern ML APIs, FastAPI’s async capabilities and automatic docs often offer practical advantages. The course currently endorses Python with FastAPI for ML model deployments (updated from Flask). If you have constraints that favor Flask (e.g., legacy codebases or specific extensions), Flask remains a viable option, but you should be prepared to add validation and docs tooling manually.