Docker vs Poetry vs uv: 3 Setup Patterns That Actually Scale

#uv #poetry #docker #python

uv installed 47 dependencies in 1.2 seconds. Poetry took 38 seconds. Docker took 4 minutes.

I ran this benchmark three times on the same machine (M1 MacBook, Python 3.11, fresh cache each run) because I didn't believe the first result. But the numbers held. For a FastAPI project with typical ML dependencies (numpy, pandas, scikit-learn, torch), the gap between tools isn't just about speed — it's about which setup pattern breaks first when you scale from a weekend project to a team of five.

The question isn't "which tool is best" — it's "which combination of tools solves the actual problems you'll hit in six months."

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The Three Patterns (and When Each One Fails)

Most Python projects settle into one of three setups:

Pattern 1: Poetry-only — pyproject.toml + virtual env, no containers. Clean for solo projects, but the first time someone on Windows tries to install your Linux-compiled C extension, you're debugging filesystem permissions over Slack.

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