[nwatson]

I've been using pipenv happily for a few years now, but on projects that don't have a huge number of dependencies (Django, DRF, MySQL/Postgres, AWS, Kubernetes, a few other random libraries), and haven't seen too much slowness. I suppose data-science projects with large dependencies that pull in many other dependencies might have more issues.

I've been the person to document setting up development environments for others in macOS (and Homebrew) with a view to deploying in Linux, and pipenv (and pyenv, and Docker/docker-compose for setting up software context/datasets) definitely overall minimized the complexity for those configuring their dev environments.

(EDIT: documenting dev enviroments)