Poetry simplifies dependency management with its unified
pyproject.toml, but Hatch excels in scenarios requiring complex workflows.
A common personal use-case is Docker multi-stage builds. Hatch, with its conventional
setup.py, offers more granular control, making complex configurations easier.
Hatch also aligns closely with the existing Python ecosystem due to its use of traditional setup files, linking old with new workflows, ensuring a better integration.
For instance, if using a container image manifest such as
# Use a base Python image
FROM python:3.9-slim as base
# Set up a working directory
# Copy requirements and install dependencies
COPY requirements.txt .
RUN pip install -r requirements.txt
# Copy the rest of the application
COPY . .
# Other Docker configurations...
Whereas with Poetry, you might need to install it within the Docker image and use
poetry export to generate a
requirements.txt equivalent, with Hatch, since it supports the traditional
requirements.txt, integration with multi-stage builds can be simpler.