Speed matters everywhere. How much compute is spent on things that could easily be 100x faster than they are? Compare using VMware with pip to run a battery of unit tests with firecracker plus uv. It’s orders of magnitude quicker, and avoids a whole suite of issues related to persistent state on the machine

Possibly for some workflows, though personally I find the emphasis on speed baffling and a big part of the reason I don’t find most of these uv testimonials credible. I’m a regular python user across multiple environments and I’ve never considered waiting for pip to be a material part of my time, it’s trivial to the point of being irrelevant. The fact that so many people come out of the woodwork to talk about how fast it is, means either there’s some big group somewhere with a niche use case that gets them bogged down in pip dependency resolving or whatever gets sped up (obviously the actual downloading can’t be faster) or it’s just a talking point that (presumably) rust zealots who don’t actually use python arrive with en mass, but it’s honestly an extremely ineffective way of promoting the product to most python users who don’t have speed of package installation as anything close to a pain point.

Yes. Technical excellence is a virtue in and of itself.

It's fast enough that sometimes dependencies can be checked and resolved and installed at program runtime rather than it needing to be a separate step.

You can go from no virtual environment, and just "uv run myfile.py" and it does everything that's needed, nearly instantly.

On my system, Pip takes noticeable time just to start up without ultimately doing anything of importance:

  $ time pip install
  ERROR: You must give at least one requirement to install (see "pip help install")

  real 0m0.356s
  user 0m0.322s
  sys 0m0.036s

(Huh, that's a slight improvement from before; I guess pip 25.3 is a bit better streamlined.)