uv vs Poetry: 16x Faster Resolver, 89K Stars [2026]

Python developers picking a dependency manager in September 2026 are really choosing between two philosophies: Poetry’s decade-old, all-in-one packaging workflow, or uv’s Rust-built speed run through the same problem. The debate shows up constantly in GitHub issues, CI logs, and Stack Overflow threads, and it usually comes down to one question: is uv actually faster in ways that matter, or is Poetry’s maturity worth the wait? uv vs Poetry is no longer a niche argument among Python tooling nerds — it’s a decision that affects onboarding time, CI minutes, and how fast a team can spin up a reproducible environment on a fresh laptop.

This comparison pulls together GitHub star counts, live PyPI download data, and independently published benchmark numbers from four separate testing sources to show exactly where uv pulls ahead, where Poetry still holds its own, and which one fits your project. We’ll cover lockfiles, Python version management, workspace support, publishing workflows, Docker and CI integration patterns, plugin ecosystems, and a full migration path from Poetry to uv, plus a pricing and total-cost-of-ownership breakdown for teams weighing the switch. Every number below comes from a live API pull, an official changelog, or a named third-party benchmark published in 2026 — nothing here is estimated.

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What Is uv, and Why Is Everyone Switching?

uv is a Python package and project manager built by Astral, the company behind the Ruff linter. It’s written in Rust, and its core pitch is that it replaces pip, pip-tools, pipx, Poetry, pyenv, and virtualenv with a single binary that does dependency resolution, virtual environment creation, and Python version management. As of September 2026, uv is on version 0.12.12, released September 9, 2026 — a release cadence that’s typical for the project, which has shipped patch or minor updates on an almost weekly basis throughout the year, according to its GitHub release history. Independent benchmarking site Pynions was still citing version 0.11.24 as the latest release as recently as June 2026, which gives a sense of how fast that version number has climbed in the months since.

uv’s headline feature is speed. It uses a global cache, a Rust-based resolver, and parallelized network requests to cut install and resolution times from tens of seconds down to a fraction of a second on cached runs. Astral’s own blog notes that uv shipped support alongside Python 3.14 on the day that release landed, underscoring how tightly the tool tracks the CPython release cycle. The project also includes uv python, a built-in Python version manager that installs and pins specific interpreter versions without requiring pyenv or a system package manager.

What Is Poetry, and Why Do Teams Still Use It?

Poetry has been the default answer to “how do I manage a real Python project” since 2018, long before uv existed. It’s a pure-Python tool maintained by an independent community team, and it bundles dependency management, virtual environment handling, and package publishing into one pyproject.toml-driven workflow. Poetry was still at version 2.3.2 as of January 2026, per Cuttlesoft’s benchmark testing that month, and has since climbed to version 2.4.3, released September 5, 2026, following the 2.4.0 release on May 3, 2026 and a 2.4.1 patch on May 9, 2026 — a slower but steady release rhythm compared to uv’s near-weekly cadence.

Poetry’s strength has always been its opinionated, batteries-included design. It generates a poetry.lock file automatically, manages virtual environments without extra configuration, and has a mature poetry publish workflow that’s been battle-tested across thousands of PyPI packages for years. Plenty of teams stick with Poetry not because they haven’t heard of uv, but because migrating a large, already-working dependency tree carries its own risk, and Poetry’s resolver — while slower — is well understood.

uv vs Poetry: Full Specs Comparison Table

Here’s how the two tools stack up across the specs that actually influence a decision, current as of September 10, 2026.

Feature	uv	Poetry
Latest version (Sept 2026)	0.12.12 (Sept 9, 2026)	2.4.3 (Sept 5, 2026)
Written in	Rust	Python
GitHub stars	89,683	34,295
GitHub forks	3,562	2,488
Monthly PyPI downloads	161.4 million	57.8 million
Backing organization	Astral (also builds Ruff, ty)	Independent “Poetry team”
Lockfile	uv.lock	poetry.lock
Built-in Python version manager	Yes (uv python install)	No — requires pyenv or system Python
Virtual environment management	Automatic, built-in	Automatic, built-in
pip-compatible interface	Yes (uv pip subcommand)	No
Workspace/monorepo model	Native workspace support, Cargo-style	No native workspace; path dependencies only
Publish to PyPI	uv publish	poetry publish
Cold install, 47 dependencies*	2.8 seconds	11.2 seconds
Dependency resolution, same test*	1.4 seconds	22.3 seconds
License	MIT / Apache 2.0	MIT
Price	Free, open source	Free, open source

*Benchmark from Danilchenko.dev, August 2026, testing uv 0.12.3 against Poetry 2.4.1 on a 47-dependency project. Full benchmark breakdown below.

Two numbers jump out immediately in any uv vs Poetry comparison: uv has roughly 2.6 times the GitHub stars and 2.8 times the monthly PyPI download volume of Poetry, both pulled live from GitHub’s API and PyPI’s official download statistics service on September 10, 2026. That’s a meaningful adoption signal for a tool that only became broadly popular in the last two years, and it tracks with the performance numbers below.

Installation and Dependency Resolution Speed Benchmarks

Speed is the single biggest reason teams cite for evaluating uv vs Poetry in 2026, so it’s worth looking at more than one benchmark. Four independently published tests, run at different times in 2026 on different hardware and dependency sets, all point the same direction.

Source and test setup	Scenario	uv	Poetry	pip / pip-tools
Danilchenko.dev (Aug 2026), 47 deps	Cold install from lock	2.8s	11.2s	33.1s
Danilchenko.dev, same test	Dependency resolution	1.4s	22.3s	35.7s
Danilchenko.dev, same test	Warm install (cached)	0.4s	3.1s	8.7s
Techplained (Apr 2026), ~80 deps, Django+Celery+Pandas+scikit-learn	Cold install	~8s	~50s	~90s (pip+venv)
DevToolLab, 200-package lockfile, M2 MacBook	Total install, warm cache	1.5s	16.0s	20.5s
Cuttlesoft “Package Manager Shootout,” Sentry dependency set	Cold install from lock	~3s	~11s	~33s (pip-tools)

The Danilchenko.dev benchmark is the most granular of the four, breaking out cold install, resolution, virtual environment creation, and warm install separately. On that test, uv resolved dependencies roughly 16 times faster than Poetry and completed cold installs about 4 times faster. The Cuttlesoft shootout, run in January 2026 using Sentry’s real production dependency list rather than a synthetic project, found numbers in the same range: a cold install from lock took uv about 3 seconds against Poetry’s 11 seconds, lockfile generation ran roughly 8 seconds on uv versus 22 seconds on Poetry (then still at version 2.3.2), and Poetry came in at roughly three times slower than uv across both measures. Techplained’s Django-plus-ML-stack test, using a heavier ~80-package tree, first clocked uv at 5-8 times faster than Poetry on large installs back in April 2026, then followed up in June 2026 with uv finishing a cold install in about 8 seconds against Poetry’s 50 seconds on the same stack.

Why the Resolver Gap Matters More Than the Install Gap

The install-time numbers get the headlines, but the resolution-time gap is the one that compounds. Every time a developer adds a package, bumps a version constraint, or merges a branch with an updated lockfile, the resolver runs again. Poetry’s resolver is single-threaded and backtracking-based; uv’s is written in Rust and parallelizes network I/O and version-constraint solving. On the Danilchenko.dev test, that difference alone — 1.4 seconds versus 22.3 seconds — is the gap between a resolver that feels instant and one that interrupts a developer’s flow. Multiply that by dozens of dependency changes per week across a team, and the resolver speed gap is arguably a bigger productivity factor than the raw install time.

Lockfiles and Reproducible Builds Compared

Both tools generate a lockfile that pins exact versions and hashes for every dependency, which is the mechanism that makes “it works on my machine” less of a recurring nightmare. uv writes to uv.lock, Poetry writes to poetry.lock, and both are meant to be committed to version control. The practical difference shows up in how fast that lockfile gets regenerated when something changes upstream — which loops back to the resolution-speed numbers above — and in how each tool handles partial updates. uv’s uv lock --upgrade-package targets a single dependency without touching the rest of the tree, similar to Poetry’s poetry update <package>, so day-to-day lockfile maintenance feels similar on both sides once a project is set up — though not identical in speed. Cuttlesoft’s January 2026 benchmark measured adding a single new package at under a second with uv versus roughly 3 seconds with Poetry, a gap small enough on its own but one that adds up across dozens of dependency bumps a week.

Where the two diverge is cold-start lockfile generation on a brand-new project with a large dependency tree — exactly the scenario the Cuttlesoft shootout measured using Sentry’s real dependency list. Poetry’s lockfile generation took roughly three times longer than uv’s in that test, a gap that matters most for large monorepos or projects with wide, deeply nested dependency graphs where the SAT-solving step is genuinely expensive.

Virtual Environments and Python Version Management

Both tools create and manage a project-local virtual environment automatically — neither requires manually running python -m venv first. That part is roughly a wash. The real differentiator is Python interpreter management. uv ships uv python, a built-in command that downloads, installs, and pins specific CPython builds without touching the system Python or requiring a separate tool. Astral’s documentation describes this as shipping a frozen, versioned set of available Python builds with each uv release, so uv python install 3.13 or a pin to Python 3.14 just works out of the box.

Poetry has no equivalent built-in interpreter manager. Teams running Poetry typically pair it with pyenv to switch between Python versions, which means an extra tool, an extra config file, and an extra thing that can drift out of sync across a team’s machines. For projects that only ever run on one pinned Python version, this doesn’t matter much. For teams juggling multiple Python versions across services — say, a legacy service on 3.10 and a new one on 3.14 — uv’s built-in version management removes a whole category of onboarding friction that Poetry still requires a second tool to solve.

Workspace and Monorepo Support

uv supports a native workspace model, conceptually similar to Cargo workspaces in Rust: multiple related packages in one repository can share a single top-level uv.lock file while still being installable and versioned independently. That’s a meaningful feature for organizations running a monorepo with several internal libraries and one or more deployable services, since it avoids maintaining N separate lockfiles that can quietly drift apart.

Poetry doesn’t have a direct equivalent. Multi-package Poetry repositories generally fall back on path dependencies between packages, with each package maintaining its own lockfile, or rely on external tooling to coordinate versions across the repo. That’s a workable pattern that plenty of large Python codebases use successfully, but it puts more of the coordination burden on the team rather than the tool itself.

Publishing Packages to PyPI

Publishing is one area where Poetry’s maturity is a genuine advantage. poetry build and poetry publish have been the standard path for open-source Python package maintainers for years, and the workflow — including trusted publishing via PyPI’s OIDC integration — is well documented and widely used across the ecosystem. uv’s uv build and uv publish commands cover the same ground and work with the same trusted-publishing flow, but they’re newer, with a shorter track record across the long tail of PyPI packages. For a brand-new library, either tool will get a package onto PyPI without drama. For a maintainer of an established package with a complex build backend or unusual packaging requirements, Poetry’s longer history may mean fewer edge cases to debug.

Adoption and Ecosystem: Stars, Downloads, and Backing

Beyond the head-to-head specs, adoption trajectory is a fair signal of which tool has momentum going into 2027. Pulled live on September 10, 2026, the numbers are lopsided:

Metric	uv	Poetry
GitHub stars	89,683	34,295
GitHub forks	3,562	2,488
PyPI downloads, last 30 days	161,400,150	57,768,745
PyPI downloads, last 7 days	28,576,395	9,551,485
PyPI downloads, last 24 hours	4,892,931	1,794,730

For context, two older tools in the same space — Pipenv and PDM — sit at 25,032 and 8,673 GitHub stars respectively, both well behind Poetry and further still behind uv. uv is backed by Astral, the same company behind the Ruff linter and the newer ty type checker, giving it a coordinated toolchain story: Ruff, uv, and ty all target the same “replace the slow Python tool with a fast Rust one” niche. Poetry, by contrast, is maintained by an independent community team without a named corporate sponsor or foundation in its public documentation — a structure that has kept it stable for years but doesn’t carry the same commercial backing that’s funding uv’s rapid release cadence.

Pricing and Total Cost of Ownership

Neither tool charges anything — uv and Poetry are both free, open-source projects with no paid tier, no enterprise license, and no usage limits. The real cost difference between them shows up in CI minutes and developer wait time, not an invoice. Techplained’s April 2026 benchmark quantified this for a Django-plus-ML-stack project with roughly 80 dependencies, estimating the annual CI time each tool saves compared to a plain pip-and-venv baseline:

Tool	Cold install (typical CI run)	Est. annual CI time saved vs. pip+venv*
uv 1.0+	~8 seconds	~58 hours
PDM 2.x	~38 seconds	~28 hours
Poetry 2.x	~50 seconds	~22 hours
pip + venv	~90 seconds	— (baseline)

*Source: Techplained, April 2026, based on a Django+Celery+Pandas+scikit-learn project running on GitHub Actions ubuntu-latest runners. Actual savings scale with how often your CI pipeline installs dependencies from a cold cache.

On that project profile, switching from pip to uv saved roughly 2.6 times more annual CI time than switching from pip to Poetry. If your team runs a metered CI provider that bills by the minute, or if your build pipeline installs dependencies from a cold cache on every pull request, that gap adds up fast — not in licensing dollars, since both tools are free, but in the CI bill and in the minutes engineers spend staring at a spinner waiting for a build to finish.

uv and Poetry in Docker and CI Pipelines

Most of the benchmark gap between uv and Poetry shows up hardest in containerized builds, where a cold cache is the default state on every run unless a team has invested in layer caching or a remote build cache. A typical multi-stage Dockerfile installs dependencies in an early layer, then copies application code on top, which means the dependency-install step runs from scratch on every CI job that touches a lockfile or base image. That’s precisely the scenario the Techplained and Cuttlesoft benchmarks measured, and it’s where uv’s 5-to-16x speed advantage over Poetry translates most directly into shorter pull-request feedback loops.

# Typical uv-based Dockerfile dependency layer
FROM python:3.13-slim
COPY --from=ghcr.io/astral-sh/uv:latest /uv /usr/local/bin/uv
WORKDIR /app
COPY pyproject.toml uv.lock ./
RUN uv sync --frozen --no-install-project
COPY . .
RUN uv sync --frozen

# Equivalent Poetry-based Dockerfile dependency layer
FROM python:3.13-slim
RUN pip install poetry==2.4.3
WORKDIR /app
COPY pyproject.toml poetry.lock ./
RUN poetry install --no-root --only main
COPY . .
RUN poetry install --only main

Both patterns follow the same layer-caching logic — copy the lockfile before the application code so Docker only re-runs the install step when dependencies actually change. The difference is what happens on a cache miss. When a base image updates or a lockfile changes, uv’s uv sync --frozen step reruns in roughly the times shown in the benchmark tables above, while the equivalent poetry install step takes several times longer on the same dependency set. For teams running dozens of pull requests a day through a shared CI fleet, that gap is the difference between a build queue that stays empty and one that backs up during a busy afternoon.

Plugin and Extension Ecosystems

Poetry has had years to accumulate a plugin ecosystem, and it shows. Community plugins exist for dynamic versioning, exporting to alternate lockfile formats, integrating with private package indexes, and shelling out to external secret managers during publish steps. Poetry’s plugin API is stable and documented, and installing a plugin is a one-line poetry self add command, which makes it straightforward for a team to bolt on exactly the behavior they need without forking the tool itself.

uv takes a different approach: rather than a plugin system, Astral has built most of the equivalent functionality directly into the core binary, on the theory that a smaller number of well-maintained built-in features beats a long tail of community plugins with inconsistent maintenance. That means fewer third-party moving parts to audit for a security review, but it also means teams with a very specific Poetry plugin dependency — say, a custom private-registry authentication flow — need to check whether uv’s built-in equivalent covers their case before migrating, since there’s no plugin marketplace to fall back on if it doesn’t.

Real-World Use Cases: When to Pick uv vs Poetry

The right answer depends heavily on what you’re building and how your team already works, not just on which tool posted the fastest benchmark number. A five-dependency internal script and a two-hundred-dependency monorepo have completely different tolerance for resolver slowness, and a team’s existing investment in Poetry-specific tooling is a real switching cost even when the performance case for uv is clear. Here’s how the two tools map onto common Python project types in 2026.

• Startup SaaS backend with a metered CI bill. A Django or FastAPI service with a fast-moving dependency tree and a CI pipeline that runs on every commit benefits directly from uv’s cold-install speed — the Techplained numbers above translate into real CI cost savings the more often the pipeline runs.
• Data science and ML teams rebuilding environments constantly. Notebook-heavy workflows where environments get torn down and rebuilt across GPU instances or Docker images benefit from uv’s sub-second warm installs and its built-in Python version pinning, which keeps CUDA-sensitive interpreter versions consistent across machines.
• Established open-source libraries with a mature publish workflow. A PyPI package that’s been using poetry publish for years, with a stable dependency tree that rarely changes, has less to gain from switching and more risk in disrupting a working release process.
• Bootcamp and classroom environments. Students installing a Python toolchain for the first time benefit from uv’s single static binary and built-in interpreter management, which sidesteps the “which Python am I even using” confusion that trips up beginners on systems with multiple Python installs.
• Enterprise monorepos with several internal packages. Organizations running multiple services and shared internal libraries out of one repository get real structural value from uv’s native workspace model, which Poetry doesn’t replicate without extra tooling.
• Freelancers and consultants juggling client-specific Python versions. Switching between a client on Python 3.10 and another on 3.14 is a single uv python pin away, without installing and configuring pyenv on top of Poetry.

Where uv and Poetry Fit Among Other Python Tools

uv and Poetry aren’t the only two options, even if they’re the two generating the most search interest in 2026. Conda remains dominant in scientific computing and GPU-heavy workflows where binary packages outside PyPI — CUDA toolkits, compiled BLAS libraries — matter more than pure Python dependency resolution, and neither uv nor Poetry directly replaces that use case. Pipenv, an earlier attempt at combining pip and virtualenv into one workflow, sits at 25,032 GitHub stars, well behind both tools compared here, and has seen its mindshare shrink as Poetry and then uv took over the “opinionated all-in-one” niche it originally defined. PDM, at 8,673 stars, occupies similar territory to uv — PEP 621-based, workspace-aware — but never reached the same adoption curve, and the Techplained benchmark shows it landing in the middle of the pack on speed, faster than Poetry but still well behind uv.

That context matters because it shows uv vs Poetry isn’t really a two-horse race that emerged from nowhere — it’s the current chapter of a longer story where the Python community has cycled through pip, virtualenv, Pipenv, Poetry, and PDM looking for a dependency workflow that doesn’t fight the developer. uv’s star and download numbers suggest it’s currently winning that longer race, not just this particular matchup, which is part of why so many teams are treating the migration decision as a “when,” not an “if.”

Migration Guide: Moving From Poetry to uv

If the benchmarks above make a strong enough case, migrating an existing Poetry project to uv is a bounded, reversible process. Here’s a safe path.

# 1. Install uv (macOS/Linux)
curl -LsSf https://astral.sh/uv/install.sh | sh

# 2. Back up your existing files before touching anything
cp pyproject.toml pyproject.toml.bak
cp poetry.lock poetry.lock.bak

# 3. Export your current Poetry dependency list as a fallback reference
poetry export -f requirements.txt --output requirements-poetry-export.txt --without-hashes

# 4. Initialize uv in the project (keeps your existing pyproject.toml where possible)
uv init --bare

# 5. Re-add your dependencies through uv so it builds a fresh uv.lock
uv add $(poetry show --only main | awk '{print $1}')

# 6. Add dev-only dependencies to the dev group
uv add --dev $(poetry show --only dev | awk '{print $1}')

# 7. Pin the Python version your project actually needs
uv python pin 3.13

# 8. Sync the environment from the new lockfile
uv sync

# 9. Run your test suite against the new environment
uv run pytest

A few things worth double-checking after that: verify that any custom build-backend settings in [tool.poetry] sections translate correctly to uv’s PEP 621-based [project] table, confirm your CI configuration calls uv sync and uv run instead of poetry install and poetry run, and re-run your full test suite at least once with a completely cold cache to make sure the new lockfile resolves identically to what Poetry was producing.

Rolling Back if Something Breaks

Because the migration keeps pyproject.toml.bak and poetry.lock.bak intact, rolling back is just restoring those two files and reinstalling Poetry’s environment with poetry install. Teams that want a lower-risk path can also run uv and Poetry side by side for a sprint or two — using uv locally for its speed while keeping Poetry as the CI source of truth — before fully cutting over. That hybrid period is a reasonable way to validate that the two lockfiles resolve to equivalent dependency trees before removing Poetry from the pipeline entirely.

Common Criticisms and Known Limitations

Neither tool is criticism-free, and it’s worth being direct about both sides before drawing a conclusion. The most consistent criticism of Poetry across the 2026 benchmark write-ups referenced throughout this piece is that its resolver simply hasn’t kept pace: articles from Techplained, Danilchenko.dev, DevToolLab, and Cuttlesoft all frame Poetry’s single-threaded, backtracking resolver as the primary bottleneck, not its virtual environment handling or CLI design, which are generally described as solid. Poetry’s own release notes show a project still actively fixing bugs and shipping features through 2.4.x, so it isn’t stagnant — it’s just architecturally slower at the one operation developers run most often.

uv’s criticisms run in a different direction. Because it moves fast — often shipping multiple releases a week — teams that pin exact uv versions in CI need to actually update that pin periodically or they lose access to bug fixes and new Python version support. Its publishing workflow, while functionally complete, doesn’t yet have the multi-year track record that poetry publish has across the long tail of edge-case build backends, unusual C-extension packages, and legacy packaging metadata that Poetry has already encountered and fixed. And because uv folds so much functionality into one binary — package management, venv handling, and Python version management — some teams have raised the fair point that it’s a bigger single point of failure in a toolchain than three separate, swappable tools would be, even though in practice that consolidation is exactly what makes it fast.

Pros and Cons of uv

Pros: dramatically faster installs and dependency resolution across every published benchmark; built-in Python version management that removes the need for pyenv; native workspace support for monorepos; a single static Rust binary with no Python bootstrapping problem; pip-compatible interface for teams migrating gradually; larger and faster-growing GitHub community with 89,683 stars and 161.4 million monthly PyPI downloads; near-weekly release cadence that tracks new CPython versions quickly.

Cons: newer publishing workflow with a shorter production track record than Poetry’s; rapid release cadence means more frequent changes to keep up with, even if most are backward compatible; smaller long-tail of community tutorials and Stack Overflow answers built up over fewer years; teams with deep Poetry-specific tooling or plugins face a real migration cost, even if the process itself is straightforward.

Pros and Cons of Poetry

Pros: mature, widely documented publishing workflow used by thousands of established PyPI packages; a stable, predictable API that hasn’t changed dramatically in years; a large existing base of tutorials, Stack Overflow answers, and internal team documentation already written against it; straightforward for teams that only ever target one pinned Python version and don’t need workspace features.

Cons: meaningfully slower dependency resolution and installs across every independent benchmark tested — roughly 4 to 16 times slower than uv depending on the operation; no built-in Python version manager, requiring pyenv or a system Python as a second dependency; no native workspace model for monorepos; slower release cadence, with only three releases in the 2.4.x series between May and September 2026; smaller and slower-growing GitHub community relative to uv.

The Verdict: uv vs Poetry for 2026

The data points in one direction for most new projects: uv wins on every published speed benchmark, has 2.6 times Poetry’s GitHub stars, pulls in 2.8 times Poetry’s monthly PyPI downloads, and includes built-in Python version and workspace management that Poetry simply doesn’t have. For a new project starting today, or for a CI-heavy team that installs dependencies from a cold cache dozens of times a day, uv is the more defensible default in 2026 — the resolver speed gap alone, 16 times faster in the most granular benchmark tested, is hard to argue against once you’ve felt it.

Poetry still earns its keep in specific situations: an established open-source package with a publishing workflow that’s worked reliably for years, a team that’s deeply invested in Poetry-specific plugins, or a project where the dependency tree rarely changes enough for resolution speed to matter day to day. Neither tool costs anything, so the decision isn’t really about price — it’s about how much your team’s time is worth, measured in CI minutes and the seconds a developer spends waiting after typing an install command. For most teams weighing uv vs Poetry in September 2026, the speed and feature gap has grown wide enough that uv is the safer bet for anything new, while Poetry remains a reasonable choice to leave alone if it’s already working.

If there’s one number to hold onto from this whole comparison, it’s the resolver gap: 1.4 seconds versus 22.3 seconds on the same 47-dependency project, measured independently in August 2026. That’s not a marginal optimization — it’s the difference between a tool a developer never thinks about and one that becomes a small daily tax. Weigh that against Poetry’s longer publishing track record and larger body of existing documentation, and the calculus for most greenfield Python work in late 2026 tips clearly toward uv, while teams sitting on a stable, working Poetry setup have little urgency to rip it out today.

Frequently Asked Questions

Is uv actually faster than Poetry in every scenario?

Across four independently published 2026 benchmarks — from Danilchenko.dev, Techplained, DevToolLab, and Cuttlesoft — uv was faster than Poetry in every test measured, including cold installs, dependency resolution, warm installs, and lockfile generation. The size of the gap varies by project (roughly 3x to 16x depending on the operation and dependency count), but the direction is consistent across every source.

Can I use uv and Poetry in the same project during a migration?

Yes, for a transition period. Since both tools can read and write a standard pyproject.toml, teams can run uv locally for day-to-day speed while keeping Poetry as the CI source of truth until the two lockfiles are confirmed to resolve equivalent dependency trees, then cut CI over to uv fully.

Does uv support Poetry’s pyproject.toml format directly?

uv uses the standard PEP 621 [project] table rather than Poetry’s legacy [tool.poetry] section, so a straight file copy won’t work. The migration path is to re-add dependencies through uv add, which rebuilds a compliant pyproject.toml and generates a fresh uv.lock, as shown in the migration guide above.

Is uv considered production-ready for enterprise teams in 2026?

With 89,683 GitHub stars, 161.4 million monthly PyPI downloads, and a near-weekly release cadence through September 2026, uv has moved well past experimental status. Its publishing workflow is newer than Poetry’s, so package maintainers with complex build requirements should test thoroughly before switching, but for dependency management and CI use, uv is widely adopted in production.

Does uv replace pyenv completely?

For most workflows, yes. The uv python install and uv python pin commands download, manage, and pin specific CPython versions without a separate tool. Astral’s documentation notes that available Python builds are frozen per uv release, so very cutting-edge or unusual interpreter builds may still require pyenv in edge cases.

What happens to my poetry.lock file when I migrate to uv?

It isn’t used by uv directly — uv generates its own uv.lock from your dependency list. The recommended approach, covered in the migration guide above, is to back up poetry.lock, export a plain requirements list with poetry export as a reference, and let uv build a fresh lockfile via uv add.

Is Poetry still actively maintained in 2026?

Yes. Poetry shipped version 2.4.0 in May 2026 and 2.4.3 in September 2026, so development is ongoing, just at a slower cadence than uv’s near-weekly releases. It remains a fully supported, widely used tool — it just isn’t gaining GitHub stars or PyPI download share as quickly as uv.

Which tool is better for a solo developer with one small project?

For a small, single-Python-version project, both tools work fine, and the speed gap matters less when there are only a handful of dependencies. uv’s built-in Python version management and single-binary install still make it the lower-friction starting point in 2026, especially for anyone setting up their first proper Python project structure.

Does switching from Poetry to uv change how I write my pyproject.toml dependencies?

The dependency specifiers themselves — package names and version constraints — stay the same, since both tools follow standard PEP 508 syntax. What changes is the surrounding table structure: Poetry’s dependencies typically live under a [tool.poetry.dependencies] section, while uv expects the standard PEP 621 [project.dependencies] list. The uv add workflow in the migration guide above handles that conversion automatically rather than requiring a manual rewrite.