Python Development

Python is the language we reach for when the hard part of the problem is not the web layer but the work behind it: data pipelines, machine-learning inference, document and image processing, scientific computation, automation and scraping, or an API whose real job is to sit in front of a model. It is also a perfectly good choice for a straight web backend. The skill is knowing which framework the problem is actually asking for, because "Python development" covers three very different tools.

Django, FastAPI or Flask — a decision with consequences

Django is the batteries-included choice: an ORM, migrations, an admin, authentication and a mature ecosystem, ideal for a content-heavy or transactional product where the team benefits from strong conventions and a back office out of the box. FastAPI is what we use for high-throughput, async-first APIs and for anything serving a model — typed request and response schemas from Pydantic, automatic OpenAPI docs, and native async that holds up under concurrent I/O. Flask remains the right answer for a small, sharply scoped service that should carry no more framework than it needs. We pick per service, sometimes running Django for the product and FastAPI for the inference tier in the same system, and we write down why.

Async is a tool, not a religion

FastAPI and modern Python make async cheap to write, which tempts teams into making everything async and then blocking the event loop with a synchronous database driver or a CPU-bound call — the worst of both worlds. We use async where the workload is genuinely I/O-bound and concurrent, keep the CPU-bound work in a process pool or a separate worker, and never mix a blocking library into an async path without noticing. Getting this boundary wrong is the most common cause of a "fast framework" that mysteriously stalls under load, and it is one of the things we are most often called in to fix.

Packaging, environments and the reproducibility problem

Python's historic weakness is that "it works on my machine" is a genuine hazard. We pin dependencies with a lockfile, standardise on a modern toolchain such as uv or Poetry, and ship in containers so the runtime is identical from a laptop to production. Type hints and mypy in CI turn Python's dynamism from a liability into something a reviewer can reason about. Tests run under pytest against real dependencies, not a wall of mocks. None of this is exotic; all of it is the difference between a Python service you can maintain and a script that has quietly become load-bearing.

When Python is fronting a model

A large share of our Python development sits between a product and a machine-learning model — whether that is a classical model you trained, an open-source model you self-host, or a hosted LLM you call. The engineering that matters there is rarely the model: it is batching and queuing inference so a slow call cannot block a request, caching aggressively, handling timeouts and fallbacks so a provider outage degrades gracefully rather than taking the product down, and keeping a clean boundary so the model can be swapped without touching the application. Celery or a task queue carries the heavy work; FastAPI serves the thin, fast edge.