The Problem

A typical Python Docker image built the "quick and easy" way often ends up at nearly 2 GB. For a simple API with just a few dependencies, that's excessive. Large images mean slower builds, slower deployments, more storage costs, and a larger attack surface.

With the right approach, the same application can be packaged into an image under 200 MB—10x smaller, more secure, and faster to build.

This guide covers three key optimizations:

• Replacing Debian with Alpine as the base image
• Using UV for fast, reproducible dependency management
• Leveraging multi-stage builds to separate concerns

The Naive Approach

Here's a typical single-stage Dockerfile that results in a bloated image:

FROM python:3.13

# Add common debug tools for local troubleshooting
RUN apt-get update
RUN apt-get install -y \
    curl \
    git \
    vim \
    wget \
    netcat-openbsd \
    procps

COPY --from=ghcr.io/astral-sh/uv:latest /uv /bin/
COPY pyproject.toml uv.lock ./
RUN uv sync --frozen --no-install-project

COPY src/ /app
WORKDIR /app

ENV PATH="/app/.venv/bin:$PATH"
ENV PYTHONPATH="/app/src"
CMD ["uvicorn", "blazing.main:app", "--host", "0.0.0.0", "--port", "8000"]

This extends from python:3.13 (which includes the full Debian distribution), installs debug tools, sets up UV, creates a virtual environment, and runs the app. The project only has three dependencies—FastAPI, psycopg2, and SQLModel—yet the final image weighs in at almost 2 GB.

The following sections break down a multi-stage approach that dramatically reduces this.