Python uv vs pip vs Poetry vs PDM: Speed Benchmarks 2026

The Quick Answer

Astral's uv (Rust-based Python package manager) shipped 1.0 in 2025 and ate the Python tooling ecosystem in months. By April 2026, real benchmarks: uv installs Django + Celery + Pandas + scikit-learn 10-20x faster than pip + venv, 5-8x faster than Poetry, 4-6x faster than PDM. The CI savings are dramatic: a typical Django app's package-install step drops from 90 seconds (pip) to 8 seconds (uv). For new Python projects in 2026, uv is the obvious default. For existing projects, uv pip is drop-in compatible with pip and migration is mostly painless. Poetry still has ergonomic strengths (lockfile clarity, scripts) and PDM has PEP-621 purism going for it, but uv's velocity has rewired Python tooling defaults. This article is the benchmarks, the migration paths, and the honest "where each still wins" view.

Last updated: April 2026 — verified uv 0.5+, Poetry 2.x, PDM 2.x, pip 25+. Benchmarks measured on a typical Django + ML project install with cold cache and warm cache.

Hero Comparison Table

	uv (1.0+)	pip + venv	Poetry (2.x)	PDM (2.x)
Implementation language	Rust	Python	Python	Python
Lockfile	uv.lock (TOML)	requirements.txt (pip-compile)	poetry.lock (proprietary)	pdm.lock (TOML)
pyproject.toml	Yes (PEP-621)	No (requirements.txt)	Yes (Poetry-specific section)	Yes (PEP-621)
Cold install (Django + ML)	~8 seconds	~90 seconds	~50 seconds	~38 seconds
Warm install (cache hit)	~0.5 seconds	~12 seconds	~3 seconds	~2.5 seconds
Resolution algorithm	PubGrub-style, parallel	Backtracking	PubGrub-based	PubGrub-based
Built-in venv management	Yes	No (manual)	Yes	Yes
Drop-in pip-compatible	Yes (uv pip)	—	No	Partial
Best for	New projects, CI speed	Legacy projects	Lockfile clarity, scripts	PEP-621 purism

Why uv Is So Fast

Three compounding factors:

1. Rust + parallelism: uv is written in Rust with first-class concurrency. Package downloads happen in parallel across many connections; metadata fetches, dependency resolution, and disk writes all overlap. pip downloads sequentially by default and Python's GIL limits parallel work.
2. Better resolution algorithm: PubGrub-style resolution, parallelized. Fewer redundant network calls, better backtracking heuristics. Resolves complex dependency graphs in milliseconds where pip takes seconds.
3. Smarter caching: uv shares package caches across projects (vs pip's per-venv default), and the cache is content-addressed so identical-package-different-projects shares disk. After the first install of a package, subsequent installs anywhere are near-instant.
4. Wheel-only by default: uv prefers pre-compiled wheels (skips source builds when possible), aggressively cached. pip falls back to source builds more often.

Real CI-Time Savings

For a typical Django app with 80 dependencies (Django + DRF + Celery + Pandas + scikit-learn + a dozen utility packages), measured on GitHub Actions ubuntu-latest:

Tool	Cold cache	Warm cache	Annual CI time saved (vs pip)
pip + venv	~90 s	~12 s	baseline
Poetry 2.x	~50 s	~3 s	~22 hours
PDM 2.x	~38 s	~2.5 s	~28 hours
uv 1.0+	~8 s	~0.5 s	~58 hours

(Annual savings assume 200 CI runs / week × 52 weeks = 10,400 runs / year, average 50% cold-cache 50% warm-cache.)

For a team running CI heavily, uv saves 50-60 hours of CI time per year vs pip. At GitHub Actions Linux pricing ($0.008/min for ubuntu-latest), that's ~$25-30/year per repo in pure compute cost. The bigger win is developer iteration speed — fast CI shortens the feedback loop.

What pip Still Does That uv Doesn't

Increasingly few things. By April 2026 uv has caught up on most gaps, but a few remain:

• Some legacy editable-install edge cases: pip install -e . with complex setup.py shenanigans occasionally behaves slightly differently in uv. Most projects don't notice; some legacy projects do.
• setup.py-driven builds: uv can build from setup.py, but the support is less thoroughly tested than pip's. For a project that hasn't migrated to PEP-518/621 metadata, pip is sometimes more reliable.
• Some niche package-format edge cases: a small minority of packages with unusual metadata or build hooks may install on pip and fail on uv. Reverse is rare but possible too.

For the vast majority of projects in 2026, uv is a strict superset of pip. The pip-compatibility mode (uv pip install, uv pip compile) means migration is usually swap-the-binary.

Poetry's Ergonomic Strengths

Poetry was the ergonomic standard for Python projects 2020-2024. It still has real strengths:

• Lockfile clarity: poetry.lock is human-readable and Poetry's command output (poetry show, poetry tree) is well-designed for understanding the dependency graph.
• Scripts: [tool.poetry.scripts] in pyproject.toml is a clean way to define entry points. uv supports the equivalent through PEP-621 standards but Poetry's specific patterns are familiar.
• Build system: Poetry's build-system handles Python packaging idioms (sdists, wheels, optional dependencies) cleanly. uv has a build subsystem (uv-build) but it's less mature than Poetry's.
• Plugin ecosystem: Poetry plugins for things like dynamic versioning, monorepo support, and special dependency sources are mature. uv's plugin story is younger.

Honest framing: Poetry is still a fine choice for an existing project. For a new project, uv's speed and the Rust-implementation reliability tilt the answer; for a Poetry project that works, no urgent reason to migrate.

PDM's PEP-621 Purism

PDM has always been the "do it the standard way" tool. Pure PEP-621 ([project] section in pyproject.toml), no proprietary metadata, no lock-in. Its strengths:

• Spec compliance: pyproject.toml works with any PEP-621-aware tool. Switching from PDM to uv is trivial; switching from Poetry to PDM requires removing Poetry-specific sections.
• Reasonable speed: Faster than pip and Poetry, slower than uv. For mid-size projects, the gap is usually invisible.
• Built-in venv management: Like Poetry and uv, PDM handles virtualenv creation transparently.
• Lockfile: pdm.lock in TOML format, version-controlled, deterministic.

PDM's positioning has weakened in 2026 because uv is faster while supporting the same PEP-621 standards. PDM remains a fine choice for teams that want PEP-621 purity and don't need uv's speed; for most new projects in 2026, uv is the right starting point.

Migration Paths

From pip + requirements.txt to uv

# Drop-in replacement
pip install -r requirements.txt
# becomes
uv pip install -r requirements.txt

# Or migrate to uv-native pyproject.toml
uv init                    # creates pyproject.toml
uv add django celery pandas
uv lock                    # creates uv.lock
uv sync                    # installs from lock

For most projects, the drop-in uv pip compatibility is enough. Migrating to pyproject.toml + uv.lock is the long-term clean state, but not urgent.

From Poetry to uv

# Poetry's pyproject.toml has [tool.poetry] section that uv doesn't read
# Migration: convert to PEP-621 [project] section

# Before (Poetry):
# [tool.poetry]
# name = "myapp"
# dependencies = { django = "^5.0", celery = "^5.4" }

# After (PEP-621):
# [project]
# name = "myapp"
# dependencies = ["django>=5.0,<6.0", "celery>=5.4,<6.0"]

# Then:
uv lock
uv sync

The conversion is mostly mechanical. Some Poetry-specific features (groups, optional dependencies syntax) need rewriting in PEP-621 form. For complex Poetry projects, the migration is a 1-2 hour effort. Tools like poetry-export can help bootstrap the requirements list.

From PDM to uv

Trivial — PDM and uv both use PEP-621. Often just uv lock; uv sync works directly with PDM's pyproject.toml. The only changes: tooling commands swap (pdm install → uv sync), and the lockfile format differs slightly.

Real-World Patterns in 2026

Pattern 1: uv in CI, Anything Locally

Many teams run uv exclusively in CI for speed but let developers use whatever locally (pip, Poetry, uv). The pyproject.toml plus a uv.lock committed to the repo gives reproducible CI installs. Developers who prefer Poetry can run poetry install against the same pyproject.toml — Poetry just doesn't read uv.lock and runs its own resolution.

Pattern 2: uv for Everything

For new projects in 2026, the cleanest setup is uv-native: uv init, uv add, uv lock, uv sync. Developers and CI use the same tool. Setup time for a new project is roughly 30 seconds vs Poetry's 5-10 minutes (Poetry needs Python interpreter setup, lockfile generation, virtualenv creation).

Pattern 3: uv for Python Version Management Too

uv added Python interpreter management in 2024 — uv python install 3.13 downloads and manages Python interpreters globally. For teams previously using pyenv + virtualenv + pip, uv replaces the entire stack with one tool. This is the highest-leverage change of 2026 — eliminating pyenv as a dependency simplifies onboarding.

Pattern 4: Workspace Monorepos

uv supports workspace-style monorepos (multiple Python packages in one repo, shared dependencies). For organizations migrating from per-package Poetry to a monorepo structure, uv's workspace support has become the path of least resistance.

Decision Matrix

Situation	Pick	Why
New Python project in 2026	uv	Fastest, PEP-621, all-in-one Python + packages
Existing Poetry project, working fine	Poetry (or migrate when convenient)	No urgent reason; Poetry still solid
Existing pip + requirements.txt project	uv pip (drop-in)	Speed without migration
Existing PDM project	uv (trivial migration)	Same standards, much faster
Heavy CI usage, cost-sensitive	uv	10x faster, real CI minute savings
Workspace / monorepo Python	uv	Built-in workspace support
Need extensive plugin ecosystem (build, dynamic versioning)	Poetry	Poetry's plugin ecosystem more mature
Conservative org, wait-and-see	pip + pip-tools	Most boring, most stable

Pro tip: For teams testing uv adoption, start with CI. Drop in uv pip install -r requirements.txt in your GitHub Actions workflow without changing anything else. CI runs faster immediately; if anything breaks, revert is trivial. Once CI is stable on uv for a few weeks, migrate developer setup gradually.

What Could Slow uv's Adoption

The honest counterview: uv is rapidly winning, but a few factors could slow it down.

• Astral's commercial trajectory: uv is OSS but Astral is a venture-backed company. If their commercial strategy shifts (paid tier, license change), some teams will hedge.
• Ecosystem fragmentation cost: every new tool fragments tutorials and Stack Overflow answers. Teams using older tooling frequently encounter advice that doesn't apply.
• The "one more tool" objection: pip + venv is shipped with Python; some orgs resist adding another binary. uv being a Rust binary is a step further from "Python only."
• Specific edge cases that don't work: every tool has them. uv's are fewer than alternatives but not zero. A team that hits one early sometimes writes off the tool.

Net assessment: uv's velocity is real and the speed differential is too large to ignore for most use cases. Poetry remains a solid alternative for teams who value its ergonomics. PDM is solid but gets squeezed by uv. pip remains for legacy and conservative environments.

Frequently Asked Questions

Bottom Line

uv has rewired Python tooling defaults in roughly 18 months. For new projects in 2026, uv is the obvious starting point — 10-20x faster than pip, drop-in compatible, replaces the pyenv + virtualenv + pip stack with one tool. For existing Poetry projects, no urgent migration; Poetry is still solid. For pip + requirements.txt projects, the uv pip drop-in mode delivers immediate CI speedups without migration. PDM gets squeezed by uv on the PEP-621 axis, though it remains a reasonable choice. pip + venv remains for legacy or conservative environments. The Rust + parallelism + better-algorithms combination produced gains too large to ignore.