The Tool Desk
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What the published speed figures mean
The figures come from Astral, the developer of uv. Its February 2024 announcement compared uv with pip and pip-tools under uncached and warm-cache conditions. Astral’s current documentation presents a rounded range for uv versus pip, but it does not make that range a like-for-like estimate for every install.
| Claim | Condition or scope | Source |
|---|---|---|
| 8–10× faster | Astral’s 2024 comparison with pip and pip-tools without caching. | Astral’s 2024 announcement |
| 80–115× faster | Astral’s 2024 comparison with pip and pip-tools using a warm cache; described for recreating a virtual environment or updating dependencies. | Astral’s 2024 announcement |
| 10–100× faster | Astral’s broad current positioning for uv versus pip; the overview does not specify a benchmark setup for this range. | Astral’s uv overview |
These are not independent measurements, and the published figures should not be read as a promise that any particular project will install at those speeds. Astral’s benchmark documentation says it benchmarks uv against earlier releases and other tools, and points to its repository for current results and methodology; that documentation page is dated August 20, 2024. See Astral’s benchmark documentation.
Why cache state changes the comparison
A cold install may need to download packages and build dependencies. Once relevant artifacts are cached, a later environment recreation or update can avoid some of that work. Astral says uv uses a global module cache to avoid re-downloading and rebuilding dependencies, and uses copy-on-write and hardlinks on supported filesystems. This helps explain why Astral’s warm-cache result is much larger than its uncached comparison; it does not mean a clean first install receives the warm-cache multiplier.
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Why default install timings may not be apples-to-apples
The tools do not do exactly the same work by default. Astral’s compatibility documentation states: “Unlike pip, uv does not compile .py files to .pyc files during installation by default (i.e., uv does not create or populate __pycache__ directories).” Pip does compile bytecode by default; uv provides --compile-bytecode to enable it. Compilation can add time during installation, while helping startup behavior in some workflows. For a fair timing comparison, decide whether bytecode compilation is part of the task and align that setting on both sides. See Astral’s pip compatibility documentation.
Define the operation before timing it, too. Resolving and downloading dependencies into a new environment is not the same workload as syncing a locked requirements file or updating an existing environment. Astral’s overview shows an example sync of 43 locked packages in which resolution took 11 ms and installation took 208 ms. That is an illustrative command output, not a pip comparison or a general runtime expectation.
How to benchmark uv against pip on your project
To find out whether uv is faster for your own dependency set, compare the same task under controlled conditions. The relevant controls follow from the documented cache and bytecode differences; they should not be mistaken for a description of every detail in Astral’s original benchmark.
- Choose one operation. Decide whether you are measuring a fresh environment install, a sync from a lock or requirements file, or an update. Use the same package versions and operation for both tools.
- Match the environment. Keep the Python interpreter, operating system, target environment, package index, network conditions, and filesystem as consistent as possible.
- Run cold and warm cases separately. Record an uncached run and a repeat with the relevant cache populated. Do not combine the results into one number.
- Align bytecode behavior. Either compare the defaults and disclose the difference, or enable compilation for uv with
--compile-bytecodeso both runs include that work. - Report the setup with the result. Include the package set, interpreter and platform, cache state, and whether compilation was enabled. That makes the number useful to someone trying to reproduce it.
Is uv a drop-in replacement for pip?
For common workflows, uv offers a pip-compatible interface with familiar install, compile, and sync commands. Astral describes it as intended to replace common pip and pip-tools workflows, but not as an exact clone. Check the options and behavior your project relies on before switching.
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