Does FastAPI Work With Docker?

Fully CompatibleLast verified: 2026-02-20

FastAPI and Docker work together seamlessly, making it trivial to containerize modern Python APIs for consistent deployment across environments.

Quick Facts

Compatibility
full
Setup Difficulty
Easy
Official Integration
No — community maintained
Confidence
high
Minimum Versions
FastAPI: 0.68.0
Docker: 19.03

How FastAPI Works With Docker

FastAPI is exceptionally well-suited for Docker deployment because it's a lightweight, stateless framework that runs on ASGI servers like Uvicorn. You define your API in Python, FastAPI generates OpenAPI documentation automatically, and Docker packages everything into a reproducible container. The typical workflow involves creating a Dockerfile that installs Python dependencies, copies your FastAPI code, and runs Uvicorn on a specific port. Since FastAPI has minimal overhead and produces fast startup times, container startup is quick—important for serverless and Kubernetes environments. The framework's async-native design aligns perfectly with containerized microservices architecture, where you often run multiple instances behind a load balancer. Docker also solves Python's dependency hell problem elegantly: pin your exact Python version and package versions in requirements.txt, and every environment runs identically. Most developers use multi-stage builds to keep final images lean, and tools like Docker Compose simplify local development with FastAPI services alongside databases and other dependencies.

Best Use Cases

Deploying FastAPI microservices to Kubernetes clusters with automatic scaling and health checks
Building and shipping REST APIs in CI/CD pipelines with consistent testing and staging environments
Running FastAPI alongside PostgreSQL, Redis, and other services locally using Docker Compose for full-stack development
Deploying machine learning inference APIs that need specific system libraries and Python versions

Quick Setup

bash
pip install fastapi uvicorn
bash
# Dockerfile
FROM python:3.11-slim
WORKDIR /app
ENV PYTHONUNBUFFERED=1
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

# main.py
from fastapi import FastAPI
app = FastAPI()

@app.get("/")
def read_root():
    return {"message": "Hello from Docker"}

# requirements.txt
fastapi==0.104.1
uvicorn[standard]==0.24.0

# Build and run
# docker build -t my-api .
# docker run -p 8000:8000 my-api

Known Issues & Gotchas

critical

Uvicorn binding to localhost (127.0.0.1) instead of 0.0.0.0 inside containers, making the API unreachable from outside the container

Fix: Always run Uvicorn with --host 0.0.0.0 in your Dockerfile CMD, or FastAPI will only listen on localhost

warning

Large Docker images from installing unnecessary dependencies or including development packages in production

Fix: Use multi-stage builds and separate requirements.txt files for production vs development dependencies

warning

Python buffered output prevents real-time logs in Docker, making debugging harder

Fix: Set PYTHONUNBUFFERED=1 environment variable in your Dockerfile

info

Timezone mismatches when using datetime functions without explicit UTC handling across containers

Fix: Use UTC internally and set TZ environment variable if locale-specific output is needed

Alternatives

  • Flask + Docker: Lighter than FastAPI but lacks automatic API documentation; simpler for minimal APIs
  • Django + Docker: Full-featured framework with ORM and admin panel; overkill for pure APIs but excellent for monolithic apps
  • Node.js/Express + Docker: Better concurrency model than Python, smaller container images, faster startup for certain workloads

Resources

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