If you're starting a new Python project in 2026, use uv. It's the fast, all-in-one default, and the ecosystem has consolidated around it. The only strong reasons to choose otherwise: you already have a working Poetry 2.x project that isn't hurting you (leave it), or you want the smallest possible tool that adds a lockfile to plain pip (that's pip-tools). Everything below is about which of those two exceptions is actually yours.

The three tools don't compete on the same axis, and comparing them by raw install speed misses the point. What separates them is how much of the stack one tool owns. On one end, pip-tools owns exactly one thing: turning your loose dependencies into a pinned lockfile. On the other end, uv owns the installer, the resolver, the lockfile, the virtualenv, and even the Python interpreter itself. Poetry sits in between, owning dependency management and packaging but handing Python-version management off to something else. Pick the tool whose scope matches how much you want to think about.

The speed story is real, but it's not the reason#

uv is written in Rust and, in Astral's own benchmarks, runs roughly 10–100× faster than pip for installs and resolution. That's not a rounding-error improvement. A cold CI install that took half a minute drops to a few seconds, and warm-cache installs feel instant because uv keeps a global deduplicated cache and parallelizes downloads.

But speed is the hook, not the substance. What makes uv the default is that the same binary also resolves, locks, creates environments, and installs Python. Poetry's pure-Python resolver is perfectly usable day to day; you notice the gap in CI and in big monorepo resolves, not when adding one package. Don't switch tools for the milliseconds. Switch for the consolidation, and enjoy the milliseconds.

The speed is the reason people try uv. The single-tool scope is the reason they keep it.

Lockfiles: three philosophies#

All three give you reproducible installs, but the artifact differs.

If your deploy target, base image, or teammate expects a requirements.txt, pip-tools' output slots in with zero explanation. If you want a lockfile that also carries platform markers and hashes without you thinking about it, uv or Poetry do more of the work.

The PEP 621 story, and why migration is cheap now#

PEP 621 standardized where project metadata lives: the [project] table in pyproject.toml — name, version, dependencies — in a tool-agnostic way. This matters more than it sounds, because it's what makes moving between tools nearly free.

uv is PEP 621-native. pip-tools reads standard pyproject.toml as input. And Poetry — historically the holdout with its own [tool.poetry] tables — added [project] support in Poetry 2.0 (January 2025), while keeping the legacy tables working. So in 2026, a project that declares its dependencies under [project] is portable: uv can read it, pip-compile can read it, Poetry can read it. The one catch: uv reads PEP 621 [project] metadata, not Poetry's legacy [tool.poetry] dependency tables. If your project predates Poetry 2.0, move the deps into [project] before migrating.

Python-version management is the real dividing line#

Here's the capability that quietly decides a lot of these choices. uv installs and pins Python interpreters itself. It downloads CPython builds, pins a version per project, and reproduces that on any machine — no pyenv, no system Python roulette. Poetry does not install Python; its own docs are explicit that you must have a compatible interpreter available, typically managed by pyenv. pip-tools doesn't touch Python versions or even virtualenvs — you create and activate those yourself.

For a solo founder, this is the difference between a one-line onboarding (uv sync and you're running on the right Python) and a README section explaining how to install pyenv, install the right CPython, and point Poetry at it. If reproducibility-from-scratch matters, uv is doing a job the other two delegate.

Workspaces and monorepos#

If you have one package, this section doesn't apply — skip to the recommendation. If you have several packages in one repo, it decides things. uv supports Cargo-style workspaces: multiple packages sharing a single lockfile and resolved together, which is exactly the monorepo model. Poetry handles multi-package setups through path dependencies, but there's no first-class workspace concept, so you assemble it. pip-tools has no notion of workspaces at all — you'd compile each package's requirements yourself and manage the seams by hand.

The recommendation#

The commands make the ergonomics concrete. Here's the same job — start a project, add a dependency, lock — in each tool, plus the migration one-liner:

# uv: init, add, lock, and install Python + deps in one tool
uv init myapp && cd myapp
uv add fastapi
uv lock
uv sync                 # reproduces the env, installs Python if needed

# Poetry equivalent
poetry new myapp && cd myapp
poetry add fastapi      # updates pyproject.toml + poetry.lock
poetry install

# pip-tools equivalent (you manage the venv + Python yourself)
python -m venv .venv && source .venv/bin/activate
echo "fastapi" >> requirements.in
pip-compile requirements.in     # -> pinned requirements.txt
pip-sync requirements.txt

# Migrating an existing project to uv (after deps are under [project])
uv lock                 # resolve current pyproject.toml into uv.lock, then: uv sync

Default pick: uv. For a solo founder shipping fast, it's the obvious call. One binary replaces pip, pip-tools, virtualenv, and pyenv; onboarding is uv sync; CI is fast; and PEP 621 keeps you portable if you ever change your mind. Nothing else in 2026 gives you that much for that little setup. (For a deeper single-tool walkthrough, see our uv tool highlight.)

Exception one: you already run Poetry 2.x and it's fine. A working, PEP 621-compliant Poetry project is not a problem to solve. Migrate only if slow CI installs or a tangle of pyenv-plus-Poetry-plus-pip-tools is actively costing you time. "uv is faster" is not, by itself, a reason to rewrite a workflow that ships.

Exception two: you want the minimum viable lockfile. If your deploy expects a requirements.txt, your Python and venv are already handled by your platform, and you distrust tools that do a lot, pip-tools is the honest choice. pip-compile and pip-sync add reproducibility to plain pip and stop there — the smallest surface area of the three, and sometimes that's exactly the feature you're buying.

Match the tool to how much of the stack you want it to own. Own all of it: uv. Own the dependency manager but not the interpreter: Poetry. Own nothing but the lockfile: pip-tools. Get that right and the speed numbers, the file formats, and the migration arguments all stop mattering.