Hatch vs uv: Which One for a New Python Project
Hatch and uv both manage Python projects and environments — how they actually differ, and why most people end up using both together
Both Hatch and uv manage a Python project end to end — environments, dependencies, and (for Hatch, primarily) packaging — which is why they get compared directly. They’re not solving quite the same problem, though.
What each one is actually for
Hatch is a PyPA-aligned project manager built around packaging: it standardizes pyproject.toml, handles versioning, builds, and publishing to PyPI, with environment management as one part of that. It’s the tool of choice when you’re building a library you intend to distribute and want to follow PyPA conventions closely.
uv is broader and faster: a single Rust-based tool that replaces pip, pip-tools, virtualenv, and (increasingly) pyenv — dependency resolution, lock files, virtual environments, and even Python version installation, all through one fast CLI. It’s less opinionated about packaging workflow specifically, and more focused on being the fastest way to get dependencies installed correctly.
Speed
Not close. uv’s Rust resolver handles what pip-based tools take 20-30+ seconds on in a fraction of a second to a few seconds. Hatch, using a Python-based resolver by default, sits far closer to pip’s speed than to uv’s. If you run installs often — CI, fresh clones, container rebuilds — this adds up fast.
Lock files and reproducibility
uv generates uv.lock automatically and gives reproducible installs immediately. Hatch doesn’t lock by default — it relies on pyproject.toml version constraints, and increasingly gets paired with uv itself as its installer to fill that gap. That pairing is worth knowing about: you don’t have to choose one exclusively.
The realistic answer
For a library you’re publishing to PyPI with a build/versioning workflow to manage, Hatch’s structure is worth it. For everything else — most application and script projects — uv alone is simpler, faster, and increasingly the default. See Poetry vs uv vs Hatch for how both stack up against Poetry as a third option.
Frequently Asked Questions
Is uv faster than Hatch?
Yes. uv is written in Rust and resolves/installs dependencies in a fraction of a second to a few seconds. Hatch is pure Python and, by default, delegates to a slower resolver — noticeably slower on any project with a real dependency tree.
Can you use Hatch and uv together?
Yes, and it’s increasingly common — Hatch handles project structure, versioning, and build/publish workflows, while uv can be configured as Hatch’s installer for faster environment setup. You get Hatch’s PyPA-standard project management with uv’s speed underneath.
Does Hatch have a lock file like uv?
Not by default. uv.lock gives uv fully reproducible installs out of the box. Hatch leans on pyproject.toml version constraints instead, and expects you to pair it with a separate locking tool if you want the same guarantee.
Which one should a beginner pick?
uv — it does more with less setup (project management, Python version management, and packaging all in one fast tool), and its defaults get you a working project immediately without deciding on a build backend first.