pyenv vs conda: Managing Python Versions
pyenv installs and switches between Python interpreter versions; conda manages whole environments including Python plus binary packages.
pyenv builds and selects Python versions per project, leaving package installation to pip or another tool. conda bundles its own Pythons and a cross-language package manager, so a single conda environment pins both the interpreter and compiled dependencies. pyenv is lean and Python-only; conda is heavier but solves native-dependency stacks in one place.
| pyenv | conda | |
|---|---|---|
| Manages | Python versions | Versions + packages |
| Install method | Build from source | Prebuilt binaries |
| Non-Python deps | No | Yes |
| Footprint | Light | Heavier |
| Best for | Python-only versioning | Full scientific envs |
In CI
pyenv builds interpreters from source, which is slow on a cold runner unless the build is cached; many CI setups instead use the runner-provided Python or setup-python. conda pulls prebuilt Pythons and packages but pays a solver cost. For a single pinned version, runner-provided Python is usually simplest; conda fits heavy native stacks.
Speed it up
Cache the built interpreter (pyenv) or the package cache (conda) keyed on your version pins. Both run on CI runners; faster managed runners shorten the build and solve steps.
The verdict
Managing only Python versions on dev machines: pyenv (or setup-python in CI). Needing a full environment with native, non-Python dependencies pinned together: conda. Many teams pin Python in CI directly and reserve conda for scientific stacks.