Python Package Managers Compared: pip, Poetry, uv, and Hatch

How pip, Poetry, uv, and Hatch actually differ, and which one to use for a new Python project in 2026

Python’s packaging landscape has genuinely improved in the last few years, but that also means there are now four reasonable answers to “what should I use,” each solving a slightly different scope of problem.

pip: the baseline, not a project manager

pip installs packages — that’s it. It doesn’t manage virtual environments, doesn’t lock dependency versions reliably without a separate requirements.txt discipline, and doesn’t handle publishing. Every other tool on this list either wraps pip or replaces its resolver entirely while still being compatible with the broader packaging ecosystem it defined.

If you’re just installing a couple of packages into an existing virtual environment for a quick script, plain pip is still fine. For an actual project — something with a name, dependencies that need locking, and a path to publishing — you want one of the tools below.

Poetry: dependency management done properly, if a bit slow

Poetry was the first widely-adopted tool to bring pyproject.toml-based dependency management, a proper lock file, and a builtin virtual environment workflow together in one place. It’s mature, well-documented, and still an entirely reasonable default. Its main drawback today is speed — Poetry’s resolver is noticeably slower than the newer Rust-based tools, which becomes noticeable on projects with large dependency trees.

uv: the fast newcomer that’s rapidly becoming the default

uv, written in Rust, does what Poetry does — dependency resolution, virtual environments, lock files — but 10-100x faster, and it also replaces pip, pip-tools, and virtualenv individually if you want to use it piecemeal rather than adopting its full project-management workflow. Its download numbers have grown extremely fast since release, and it’s increasingly the default recommendation for new projects specifically because of that speed difference — a uv sync that takes 2 seconds instead of Poetry’s 30+ seconds on a large project isn’t a marginal improvement, it changes how often you’re willing to rebuild an environment.

Hatch: packaging-focused, PyPA-endorsed

Hatch consolidates project management, environment handling, and publishing, with an emphasis on being the officially blessed direction from the Python Packaging Authority. It’s a strong choice if you specifically want to stay close to PyPA’s recommended tooling direction rather than a fast-moving independent project, though it hasn’t seen the same explosive adoption uv has.

A straightforward recommendation

For a new project starting today: uv. It’s fast, actively developed, compatible with standard pyproject.toml, and increasingly what you’ll see recommended in current tutorials and documentation. Poetry remains a completely reasonable choice if you’re already using it and have no specific complaint about speed — there’s no urgent reason to migrate an existing well-functioning Poetry project. Hatch is worth considering specifically if PyPA alignment matters to your team’s tooling philosophy.

What about pipx?

pipx solves a different, narrower problem — installing standalone CLI tools (like black, httpie, or poetry itself) in isolated environments so they don’t pollute your global Python installation. It’s not a project dependency manager; use it alongside whichever of the above you choose for your actual project dependencies.

The bottom line

These tools aren’t mutually exclusive across your workflow — many developers use uv or pipx for installing standalone tools globally, while using Poetry or uv’s project mode for actual project dependency management. Pick one for project management, and don’t spend more time comparing than the decision actually warrants — all four are legitimate, well-maintained choices, and switching later, while some friction, is not a rewrite.