Poetry vs uv vs Hatch: Which Python Packaging Tool in 2026

A head-to-head comparison of Poetry, uv, and Hatch — speed, lockfile behavior, and PEP 621 compliance, for picking a new project's tooling

Our packaging tools guide covers pip, Poetry, uv, and Hatch broadly. This is the narrower question people actually search for once they’ve ruled out pip: picking between the three real project-management contenders.

Speed: uv wins, decisively

This is uv’s entire pitch and it delivers. Written in Rust, uv sync resolves and installs a dependency tree in a fraction of a second to a few seconds on projects where Poetry takes 20-30+ seconds. Hatch, being pure Python and delegating to pip’s resolver by default, sits closer to Poetry than to uv on raw speed. If your team runs install dozens of times a day — CI, fresh clones, Docker rebuilds — this compounds into real time saved, not a benchmark curiosity.

Lockfiles: all three have one, details differ

• Poetry uses poetry.lock, mature and stable, resolved by Poetry’s own resolver.
• uv uses uv.lock, a newer format but already stable since uv hit 1.0, resolved by uv’s Rust resolver — the same one responsible for the speed advantage.
• Hatch doesn’t lock dependencies by default the way Poetry and uv do; it leans on pyproject.toml version constraints and expects you to pair it with a separate locking tool (often uv, used underneath) if you want a fully reproducible lockfile.

If reproducible builds matter to you out of the box, Poetry or uv give you that immediately. Hatch requires more assembly.

PEP 621 compliance

PEP 621 standardized how project metadata is declared in pyproject.toml. Hatch was built around it from the start and is the tool officially endorsed by the Python Packaging Authority for staying close to the standard. uv also supports standard pyproject.toml metadata and interoperates cleanly. Poetry historically used its own [tool.poetry] metadata section instead of the standardized [project] table, though recent versions have moved toward PEP 621 compliance — worth checking the current Poetry release notes if this specifically matters to you.

Publishing to PyPI

All three can build and publish packages. Hatch’s publishing workflow is the most polished of the three specifically because packaging (not just dependency management) is its core focus — plugin-based builds, version bumping, and environment matrices for testing across Python versions are first-class features. Poetry and uv both handle straightforward publish workflows well but are less oriented toward complex multi-environment build matrices.

Which one for a new project

• Fastest iteration, simplest mental model, most momentum right now → uv
• Already comfortable with Poetry, no specific speed complaint → stay on Poetry, no urgent reason to migrate
• PyPA-standard alignment matters, or you’re building a library with a real publishing/testing matrix → Hatch

There’s no wrong answer among the three for most projects — the actual cost of switching later is a pyproject.toml edit and a re-lock, not a rewrite.

What about plain pip, or conda?

Plain pip isn’t a peer to any of these three — it installs packages and nothing else, with no built-in lock file or virtual environment management, which is exactly why Poetry, uv, and Hatch exist. Conda is a different case: it’s a general-purpose package and environment manager that also handles non-Python dependencies (compiled C libraries, CUDA toolkits), which matters specifically for data science and ML stacks. If you’re doing GPU-heavy ML work, conda’s ability to manage those system-level dependencies is a genuine reason to reach for it instead of, or alongside, uv or Poetry — for a typical web or CLI project with only Python dependencies, it’s unnecessary overhead.