Python Development

Python is the language we pick when the hard part of the problem is not the website 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 choice with everything included: an ORM, migrations, an admin, login and a mature set of tools. It is ideal for a content-heavy product or one with many orders, where the team gains from strong rules and a ready-made back office. FastAPI is what we use for fast, async-first APIs that handle many requests, and for anything serving a model. It has typed request and response schemas from Pydantic, automatic OpenAPI docs, and async that holds up when many things happen at once. Flask is still the right answer for a small service with a narrow job that needs no more framework than necessary. 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 easy to write. This tempts teams to make everything async and then block the event loop with a synchronous database driver or a heavy CPU call. That is the worst of both worlds. We use async where the work is truly I/O-bound and happens at the same time. We keep the CPU-heavy work in a process pool or a separate worker. We never mix a blocking library into an async path without noticing. Getting this line wrong is the most common reason a "fast framework" mysteriously stalls under load. It is one of the things we are most often called in to fix.

Packaging, environments and the reproducibility problem

Python's old weakness is that "it works on my machine" is a real danger. We pin the versions of all packages with a lockfile, use a modern toolchain such as uv or Poetry, and ship in containers so the setup is the same from a laptop to production. Type hints and mypy in CI turn Python's flexibility from a risk into something a reviewer can understand. Tests run under pytest against real dependencies, not a wall of mocks. None of this is unusual. All of it is the difference between a Python service you can keep up and a script that has quietly become something everyone depends on.

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 classic model you trained, an open-source model you host yourself, or a hosted LLM you call. The work that matters there is rarely the model. It is grouping and queuing requests so a slow call cannot block a request, caching a lot, handling timeouts and backups so a provider outage slows things gently and does not take the product down, and keeping a clean line so the model can be swapped without touching the application. Celery or a task queue carries the heavy work. FastAPI serves the thin, fast front.