# Hatch vs PDM: Python Project Managers Compared > Hatch vs PDM compared on environments, lockfiles, build backends, and PEP standards. Which Python project manager fits your workflow and CI. Source: https://latchkey.dev/learn/tool-comparisons/hatch-vs-pdm Updated: 2026-06-30 Pick PDM if you want a fast resolver with a built-in lockfile and PEP 582-style local installs; pick Hatch if you want a standardized build backend, environment matrix, and project scripting from one tool. Hatch (from the PyPA) and PDM are both modern, standards-based Python project managers that replace the older setup.py plus virtualenv plus pip flow. They overlap a lot but emphasize different things: Hatch centers on reproducible environments and its hatchling build backend, while PDM centers on a fast dependency resolver and a committed lockfile (pdm.lock). Both read pyproject.toml. ## Comparison | | Hatch | PDM | | --- | --- | --- | | Config | pyproject.toml ([tool.hatch]) | pyproject.toml ([tool.pdm]) | | Lockfile | No built-in lock (uses hatch-pip-compile or external) | pdm.lock built in | | Build backend | hatchling (popular standalone backend) | pdm-backend | | Environments | Named envs and a test matrix | Single project venv (in-project .venv) | | Scripts/tasks | hatch run + scripts | pdm run + scripts | | Standards | PEP 517/518/621 | PEP 517/518/621, PEP 582 history | ## Where each genuinely wins PDM wins when you want a committed cross-platform lockfile and a quick resolver without bolting on extra tools; its `pdm.lock` and `pdm install` flow are reproducible out of the box. Hatch wins when you publish packages and want a clean, widely adopted build backend (`hatchling`) plus a matrix of test environments managed by one CLI. Many projects use `hatchling` as their build backend even when their day-to-day workflow is pip or uv. ## In CI PDM is convenient in CI because `pdm install` restores an exact, locked environment from `pdm.lock`. With Hatch, lock reproducibility usually comes from pinning in `pyproject.toml` or pairing it with `pip-tools`/`uv`; its strength in CI is running the same `hatch run test` matrix locally and on the runner. ## Honest caveats Neither is universally "better". Hatch deliberately leaves locking somewhat external, which some teams dislike. PDM bundles more (lock, scripts, plugins) but that means more PDM-specific concepts to learn. The fast-moving uv tool overlaps with both, so evaluate it before committing. ## Decide with your own numbers, not a feature table Feature comparisons age badly and rarely decide anything, because both tools in a mature category can do the job. What differs is how each behaves on your repository, and that takes one afternoon to measure. ```Terminal # time a cold install with each candidate, cache cleared hyperfine --prepare "rm -rf node_modules" --warmup 1 \ " install" " install" # and the thing CI actually pays for: a cold run with no local cache docker run --rm -v "$(pwd):/w" -w /w node:22 sh -c " install" ``` > Measure the cold path. Warm local benchmarks favour whichever tool you already have cached, which is exactly the condition a CI runner never has. ## What actually changes when you switch - Lockfile format. A switch is a one-way door for anyone still on the old tool until everyone migrates, so plan it as a single coordinated change. - Resolution strictness. Tools differ on whether an undeclared transitive import works, and the stricter one will surface latent bugs as new failures. - CI cache configuration. The cache path and key differ per tool; carrying over the old ones silently disables caching. - Everyone on the team and every runner must move together. Pin the version so they cannot drift. ## The verdict For library authors who want a standard build backend and an env matrix, Hatch fits well. For application teams who want a built-in lockfile and a fast resolver in one tool, PDM is the more complete default. ## FAQ ### Hatch vs PDM: Python Project Managers Compared? Hatch (from the PyPA) and PDM are both modern, standards-based Python project managers that replace the older setup.py plus virtualenv plus pip flow. They overlap a lot but emphasize different things: Hatch centers on reproducible environments and its hatchling build backend, while PDM centers on a fast dependency resolver and a ### Where each genuinely wins? PDM wins when you want a committed cross-platform lockfile and a quick resolver without bolting on extra tools; its pdm.lock and pdm install flow are reproducible out of the box. Hatch wins when you publish packages and want a clean, widely adopted build backend (hatchling) plus a matrix of test environments managed by one CLI. ### In CI? PDM is convenient in CI because pdm install restores an exact, locked environment from pdm.lock. With Hatch, lock reproducibility usually comes from pinning in pyproject.toml or pairing it with pip-tools/uv; its strength in CI is running the same hatch run test matrix locally and on the runner. ### Honest caveats? Neither is universally "better". Hatch deliberately leaves locking somewhat external, which some teams dislike. PDM bundles more (lock, scripts, plugins) but that means more PDM-specific concepts to learn. The fast-moving uv tool overlaps with both, so evaluate it before committing. ### Which should I choose? For library authors who want a standard build backend and an env matrix, Hatch fits well. For application teams who want a built-in lockfile and a fast resolver in one tool, PDM is the more complete default. --- Latchkey runs CI/CD that repairs its own failures. Agent entry points: https://latchkey.dev/agent.txt, https://latchkey.dev/openapi.json, https://latchkey.dev/llms.txt