uv vs Poetry vs pip: Picking a Python Package Manager Now That OpenAI Owns uv
OpenAI's acquisition of Astral, the company behind uv and Ruff, in March 2026 changed how seriously I take uv as a long-term infrastructure bet rather than just a faster pip replacement worth trying casually. Here's how the three tools actually compare after migrating a real, mid-sized project off Poetry.
pip, Still the Baseline Everyone Compares Against
pip alone handles installation but not environment management, dependency locking, or Python version switching, which is why most teams pair it with virtualenv and, historically, pip-tools for locking. It's the tool with the fewest surprises and the widest compatibility, but it also asks you to assemble your own workflow from separate pieces rather than offering one.
Poetry, the Tool We Were Actually Migrating Away From
Poetry's dependency resolution and lockfile handling were a genuine improvement over raw pip when we adopted it years ago, and its pyproject.toml-centric workflow set a pattern the rest of the ecosystem has since followed. Our specific frustration was resolution speed on a dependency graph with a meaningful number of packages, where a full lock regeneration had grown slow enough to be a genuine friction point in CI.
# Poetry
poetry add fastapi
poetry install
poetry run pytest
uv's Actual Speed Difference, Measured on Our Project
Running a full dependency install from a cold cache, uv completed dramatically faster than the equivalent Poetry install, and re-resolving the lockfile after adding a new dependency dropped from a genuinely noticeable wait to something close to instant. This wasn't a marginal, theoretical improvement, it changed how often developers on the team were willing to add or update a dependency without dreading the wait.
# uv, the equivalent workflow
uv add fastapi
uv sync
uv run pytest
Python Version Management, Genuinely Consolidated
uv installing and managing Python interpreter versions directly, alongside dependencies, eliminated pyenv as a separate tool in our toolchain entirely, and new team members onboarding now run a single tool rather than juggling pyenv, Poetry, and pip across different parts of the setup process.
uv python install 3.12
uv python pin 3.12
The Migration Itself, More Straightforward Than Expected
uv reads existing pyproject.toml files and can work alongside a Poetry-generated one during a transition period, and converting fully took an afternoon rather than the multi-day migration I'd budgeted for, mostly spent verifying the regenerated lockfile resolved to identical package versions as our existing Poetry lock.
Where I'm Still Cautious
uv is younger than Poetry as a mature tool, and while the OpenAI acquisition signals real, committed investment going forward, I've kept a documented fallback path to pip plus pip-tools in case a specific edge case in our dependency graph surfaces a bug that doesn't get resolved quickly, a caution I wouldn't have needed with the more battle-tested Poetry.
Legacy Package Compatibility
A handful of older, setup.py-based packages in our dependency tree needed the uv pip compatibility layer rather than uv's native commands to install cleanly, a reminder that the ecosystem hasn't fully caught up to the newer pyproject.toml-first world uv is built around.
CI Pipeline Impact
Beyond local developer experience, uv's speed directly reduced our CI build minutes meaningfully, since dependency installation had been a real, measurable chunk of every pipeline run, and that cost reduction alone justified the migration on its own beyond any developer experience improvement.
My Actual Recommendation
For a new project starting today, I'd choose uv without much hesitation, particularly given the backing it now has. For an existing, stable Poetry setup with no acute pain point, the migration is genuinely worth doing but isn't urgent, and for teams specifically wary of newer tooling, pip plus pip-tools remains a completely reasonable, conservative choice that isn't going anywhere.
