Python's dependency management is notoriously fragile - different pip versions, missing system libraries, and virtualenv confusion make deployments unpredictable. We containerize Python apps with locked dependencies, slim base images, and properly configured virtual environments that reproduce identically every time.
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We implement, you ship. Async, documented, done in days.
Python applications depend on the correct interpreter version, C extensions compiled against the right system libraries, and a clean virtualenv. A missing libpq-dev for psycopg2, a mismatched OpenSSL version, or a stale pip cache can silently break your deployment.
Docker pins every layer of this stack: the OS, the Python version, the system libraries, and the exact package versions from your lockfile. No more debugging production issues that vanish when you try to reproduce them locally.
We support both pip and Poetry workflows with a two-stage Dockerfile:
FROM python:3.12-slim AS builder - installs build-essential and system deps needed for C extensions, creates a virtualenv at /opt/venv, runs pip install --no-cache-dir -r requirements.txt (or poetry install --only main --no-interaction).FROM python:3.12-slim - copies only /opt/venv from the builder, installs minimal runtime system deps (e.g., libpq5 without -dev), sets ENV PATH="/opt/venv/bin:$PATH", and runs as a non-root user.For Gunicorn/Uvicorn apps, the CMD includes --workers calculated from WEB_CONCURRENCY and --bind 0.0.0.0:8000. Health checks hit the /health endpoint with curl or a lightweight Python script.
Dockerfile with slim base - typically 120-180 MB final imagedocker-compose.yml with health checks, volume mounts for dev, and env injection--reload for Uvicorn/Gunicorn)Skip the research. Tell us what you need, and we'll scope it, implement it, and hand it back — fully documented and production-ready.