The Performance Revolution
`uv`'s performance is its most notable feature, a step-change improvement over `pip`. This is not just about speed, but about enabling more efficient workflows, especially in CI/CD where every second counts. The difference is most pronounced with warm caches, reflecting real-world development cycles.
Installation Time (seconds)
A direct comparison of `pip` and `uv` installation times. Note the logarithmic scale used to visualize the vast difference.
Virtual Environment Creation Time (ms)
Near-instantaneous environment creation with `uv` makes them feel lightweight and disposable. (Lower is better).
A Tale of Two Workflows
Setting up a project and adding a dependency highlights the core philosophical difference: `pip`'s manual, multi-tool approach versus `uv`'s automated, integrated system that manages environments and dependencies in a single, atomic operation.
Traditional `pip` + `venv` Workflow
python -m venv .venvManually create an isolated environment.
source .venv/bin/activateManually activate the environment in the shell.
pip install requestsInstall package into the active environment.
(Add "requests" to requirements.in)Manually declare the direct dependency.
pip-compile > requirements.txtManually generate a pinned, platform-specific lock file.
Modern `uv` Workflow
uv add requests`uv` handles everything in one atomic step:
- Detects or creates a virtual environment.
- Adds `requests` to `pyproject.toml`.
- Resolves the complete dependency graph.
- Updates the universal `uv.lock` file.
- Installs `requests` into the environment.
Resolver Architecture
`uv`'s holistic, PubGrub-based resolver analyzes the entire dependency graph at once, preventing conflicts. `pip`'s backtracking resolver works sequentially, which can lead to more complex conflict resolution paths.
pip (Backtracking)
uv (Holistic)
Finds the optimal path directly, providing clearer error messages on failure.
Ecosystem Consolidation
uv replaces a fragmented set of tools with a single, powerful binary, simplifying the developer toolchain and reducing cognitive overhead.
Before
After
Feature Deep Dive: Reproducibility
True reproducibility is critical for collaborative development and reliable deployments. `uv`'s native, universal lockfile provides a much stronger guarantee of consistency than traditional `requirements.txt` files.
| Feature | pip + pip-tools | uv |
|---|---|---|
| Lockfile Format | `requirements.txt` | `uv.lock` (TOML) |
| Platform Portability | Platform-specific (requires separate locks or markers) | Universal (contains all platforms/py-versions) |
| Conflict Errors | Can be verbose and hard to trace | Clear, concise, and actionable |
| Resolution Strategy | Always latest compatible version | Supports `lowest` for library testing |
| Cross-Environment Resolve | No Resolves for current environment only | Yes Can resolve for any target |
⚙️ Migration Assistant
Accelerate your migration from `pip` to `uv`. Paste your `requirements.txt` content below and get instant conversions right in your browser.
Your output will appear here...The Verdict: A Framework for Choice
The choice depends on project needs and team priorities. `pip` remains the stable incumbent for legacy systems, while `uv` is the clear and compelling choice for all modern Python development.
Stick with `pip` for:
- Maintaining legacy projects with stable, embedded workflows.
- Conservative environments prioritizing maximum stability over performance.
- Projects relying on niche legacy features like `.egg` installs.
Recommended: Adopt `uv` for:
- All new Python projects, without exception.
- Large projects with complex dependencies (e.g., AI/ML, web backends).
- Optimizing CI/CD build times to reduce costs and accelerate feedback.
- Teams seeking a simplified, modern, and ergonomic developer workflow.