The Hidden Cost of Python Environments (Backed by 2025 Data)

Last month I discovered 26 dormant Python environments silently eating 45GB on my machine. That experience led me to build PyEnvManager, which I introduced in my first post.

At first, I thought this was just my bad housekeeping. But the 2025 State of Python survey by JetBrains and the PSF makes it clear: I'm not alone. Environment sprawl and dependency clutter are among the most common pain points across the Python ecosystem.

Why Python Environments Multiply

1. Too Many Environment Managers

Python has multiple competing ways to isolate environments:

• Conda / Mamba → includes a package manager and comes with its own Python builds.
• venv → standard library tool since Python 3.3.
• uv (by Astral) → modern package installer with global caching.

Each has trade-offs. For example:

• Conda provides conda env list, but venv and uv don't expose an equivalent built-in listing command. Once you've created a few venvs, they disappear into random project folders unless you track them manually.
• uv's caching is efficient (similar to pnpm in Node.js), but caching only solves disk usage — not visibility.

2. AI Tools and Silent Environments

The new wave of AI coding agents (AutoGPT, Copilot, windsurf, LangChain notebooks) often create isolated venvs in the background. These don't show up anywhere obvious, and many devs don't realize they exist until disk space is gone.

Example:

python -m venv /tmp/autogpt_env

Created automatically by an agent — never documented, never deleted.

3. Dependency-Heavy Workflows

According to the State of Python 2025:

• 51% of Python developers use it for Data Science & AI, where heavy packages like TensorFlow, PyTorch, pandas, and NumPy bloat environments.
• 83% of developers are not on the latest Python version, meaning old envs pile up as new ones are created.
• 68% of developers value tools that reduce manual environment and dependency management, reflecting a desire for automation.

Why It Matters

1. Disk Usage
   Multiple copies of scientific packages add up quickly. Even uv's caching only helps for newer installs.
2. Security
   Old envs often contain dependencies with unpatched CVEs. The survey highlighted automation as a key demand — but automation only helps if you know those environments exist.
3. Reproducibility
   Without clear tracking, projects break months later when you forget which env they used. For teams, this extends into compliance and reproducibility challenges.

A Technical Fix: PyEnvManager

That's why I built PyEnvManager (v0.3.0) — not to replace Conda, venv, Mamba, or uv, but to sit on top as a visibility + management layer. Much like Docker Desktop for Python environments.

Features in v0.3.0:

• Scan environments across Conda, venv, Mamba, and uv.
• Show Python version, size, cleanup potential, and CVEs.
• One-click Jupyter launch in the right kernel.
• Safe deletion with preview of space recovered.
• Create new envs with templates.

Looking Ahead

The State of Python 2025 makes one thing clear: the community values simplicity and automation. Developers don't want to spend hours manually cleaning environments — they want a tool that just works.

That's the philosophy behind PyEnvManager:

• No hunting for .venv folders.
• No guessing which Conda env is safe to delete.
• No manually checking CVEs.

Just a single dashboard that shows everything.

Closing Question

How many environments do you think you have? And how many do you actually have?

If you're brave enough, scan your machine — and let me know the number.