Both **Poetry** and **Hatch** are modern Python project management tools — they aim to simplify package management, virtual environments, dependency resolution, and publishing. Let’s break down their differences and use-cases so you can choose what’s best for you. --- ## 🔍 At a Glance |Feature|🧪 Hatch|🎭 Poetry| |---|---|---| |Project file|`pyproject.toml`|`pyproject.toml`| |Virtualenv management|Built-in|Built-in| |Dependency resolution|Fast (via backend like `uv`)|Slower (classic resolver)| |Build backend|Native (`hatchling`)|Native| |Plugin system|Powerful + extensible|Limited plugin support| |Multi-environment support|Native|Basic (`poetry env use`, etc.)| |Versioning (semver, etc.)|Built-in (via `version.source`)|Basic (`poetry version`)| |Publishing|Easy (`hatch publish`)|Easy (`poetry publish`)| |Config complexity|Declarative, flexible|Declarative, stricter| |Active development|Very active (modern direction)|Stable, still maintained| |Performance|Fast (especially with `uv`)|Moderate| --- ## 🧪 Hatch – Key Points ### ✅ Pros: - Super fast builds and installs with backends like [`uv`](https://github.com/astral-sh/uv) - First-class support for **multiple environments** (`dev`, `test`, `ci`, etc.) - Highly customizable with its plugin system - Flexible versioning (git, file, manual, etc.) - Advanced features like dynamic metadata, templates, etc. ### ❌ Cons: - Slightly steeper learning curve - Less beginner-friendly than Poetry out of the box - Requires more knowledge of Python packaging standards to unlock full power --- ## 🎭 Poetry – Key Points ### ✅ Pros: - Simple and intuitive interface - Great for small to mid-size projects - Virtualenv + dependency resolution is handled automatically - Very popular with beginner-to-intermediate Python developers - Cleaner error messages ### ❌ Cons: - Slower dependency resolution (though improving) - Less flexible for complex multi-environment workflows - Plugin support is minimal - Configuration is more rigid than Hatch --- ## ❓ Are They Exclusive? Yes and No: - Both tools **use the same project structure**: `pyproject.toml`, so technically, you **shouldn’t use both together** in a single project. - They serve the **same purpose**, so pick **one** per project. - However, you can use **Poetry on one project** and **Hatch on another** on the same machine — no problem. --- ## ⚖️ Which One Should You Use? ### 🔰 If you're: - New to Python packaging - Want simple CLI commands to create & manage projects - Don’t need highly customized dev/test environments 👉 Go with **Poetry** – it's clean and beginner-friendly. --- ### 🧙 If you're: - Building multi-env projects (e.g. `dev`, `staging`, `prod`) - Want faster builds (especially with `uv`) - Want to script or extend workflows (like hooks, templates) - Comfortable with deeper packaging config 👉 Use **Hatch** – it’s more powerful and future-ready. --- ## 🧪 Bonus: Can You Mix `hatchling` with `poetry`? You technically can: - Use `poetry` to manage deps - Use `hatchling` as the build backend …but that’s uncommon and can cause confusion. It's best to **stick to one ecosystem** per project.