Python & FastAPI

Why FastAPI

Python is everywhere. Data scientists use it. ML engineers use it. DevOps teams write automation in it. When your product needs to connect to that ecosystem — inference endpoints, data pipelines, analytics — you need Python on the backend. FastAPI is how we build Python backends without sacrificing performance.

FastAPI combines Python’s readability with modern async performance. It’s one of the fastest Python frameworks available, competing with Node.js and Go for raw speed on I/O-bound workloads. The secret is ASGI and Starlette under the hood, plus a design that doesn’t add unnecessary overhead. Benchmarks put it at 10x faster than Flask for concurrent requests.

But speed isn’t why we chose FastAPI. Type hints are. Python’s optional typing becomes mandatory in our codebases. Pydantic models validate every request. Your IDE autocompletes everything. Refactoring is safe. Junior developers can’t accidentally break the API contract. The productivity gains compound over months. Code that would take debugging in Flask just works in FastAPI because the types caught the problem at development time.

When We Reach for Python

Python shines when you need to integrate with data science, ML models, or existing Python libraries. If your backend needs to run inference, process data, or connect to Python-heavy infrastructure, FastAPI is the right choice.

We build Python backends when clients have data scientists who need to deploy models. When the analytics team already has Python notebooks that need to become production services. When you’re connecting to libraries that only exist in Python — computer vision, NLP, scientific computing. Don’t fight the ecosystem. Use the language your dependencies demand.

Python also wins for rapid prototyping with non-technical stakeholders. The syntax is close to pseudocode. Business logic reads like plain English. When founders need to understand what the code does, Python makes that possible.

What We Build With It

ML model serving is our most common use case. You’ve trained a model in PyTorch or TensorFlow. Now you need an API that accepts requests, runs inference, and returns predictions. FastAPI handles the web layer while your model does the heavy lifting. We’ve deployed image classification, NLP pipelines, and recommendation engines this way.

Data processing pipelines turn raw data into useful products. Ingesting CSVs from partners. Transforming and validating records. Loading into analytics databases. FastAPI handles the HTTP endpoints while Pandas, Polars, or custom Python code does the transformations.

REST APIs for dashboards and internal tools. Admin panels that need to query databases and return JSON. Reporting endpoints that aggregate data. Webhook receivers that process events from third-party services. FastAPI makes these straightforward.

Integration layers that connect systems. Your main application is in Node.js or Elixir, but you need to call a Python ML model. FastAPI becomes the bridge — a clean API that hides Python complexity behind HTTP endpoints.

Our Approach

We structure FastAPI projects for maintainability. Routers group related endpoints. Services contain business logic. Repositories handle database access. This separation means you can test business logic without spinning up HTTP servers or databases.

Dependency injection is core to how we build. Database sessions, authentication, configuration — all injected. Testing becomes trivial. Mock the dependencies, test the logic. No monkeypatching, no globals, no test pollution.

Async matters for I/O-bound operations. Database queries, HTTP calls to external services, file operations — all async. But we don’t async everything. CPU-bound code stays synchronous. Mixing them wrong creates deadlocks and confusion. We know where the boundaries are.

Type hints aren’t optional in our codebases. Every function has return types. Every parameter is annotated. Pydantic models define every request and response. MyPy runs in CI. If it doesn’t typecheck, it doesn’t ship.

Performance Reality

FastAPI is fast for Python, but it’s still Python. CPU-bound work will hit limits. Image processing, heavy computation, tight loops — Python isn’t the answer. We use worker processes, background tasks, or offload to compiled code when needed.

For I/O-bound APIs, FastAPI holds its own against any framework. Database queries, external API calls, file reads — the async event loop handles thousands of concurrent connections efficiently. We’ve run benchmarks against Node.js and Go. FastAPI competes.

Common Challenges

Python dependency management used to be a nightmare. Poetry solved it. We lock dependencies, separate dev from production, and reproduce environments reliably. No more “works on my machine” problems.

Scaling CPU-bound workloads requires thought. We use Celery for background jobs, or separate worker processes for heavy computation. The API stays responsive while workers handle the load.

Cold starts matter for serverless deployment. Python isn’t the fastest to start. We mitigate with warm-up requests, keep containers alive, or accept the latency for appropriate use cases.

Production debugging requires setup. We instrument with OpenTelemetry from day one. Structured logging with correlation IDs. Distributed tracing across services. When something breaks at scale, we can trace the exact request through the entire system.