Python

Django suits a product with accounts, permissions, an admin surface and a relational schema: most of that arrives built and reviewed rather than written from scratch. FastAPI suits services whose job is an API: it is lighter, async-first, and generates its documentation from the code.

The two coexist well. A common shape is a Django core with FastAPI services beside it for high-concurrency or model-serving endpoints, split along that line rather than forcing either framework to do the other's job.

For most APIs, yes. The bottleneck in a typical service is the database or a downstream call, and an async Python service spends its time waiting on those whatever the language. On Uvicorn, an I/O-bound FastAPI service holds thousands of requests a second.

Where Python is not fast enough is sustained CPU-bound work. If an endpoint computes rather than coordinates, we profile first, then move that path to Go, Java or C++ while Python keeps the rest of the service.

Yes. Both providers ship first-class Python SDKs, and the usual pattern is a thin seam over the calls your application makes, so models can be swapped or mixed per task. Prompts and evaluations are versioned per model, because a prompt tuned for one does not transfer unchanged to another.

Yes, with one caveat. Python has the densest public code corpus of any language, so coding agents produce idiomatic Python with fewer corrections than almost any other stack. The caveat is the correctness signal: dynamic typing gives an agent less to check its work against than a compiled, typed language does.

The remedy is the discipline we apply anyway: strict mypy, a fast test suite, and Ruff in CI. With those in place an agent gets a quick, checkable feedback loop. The measured results of our AI-augmented delivery flow are on the Technology page.

It depends what the codebase gives the tooling to check against. A legacy system with tests and type hints is a strong candidate: an agent can read it, change it, and verify the change. A large untyped codebase without tests gives an agent nothing to verify against, so the first step is adding characterisation tests and types to the paths being changed.

That first step is ordinary engineering work, and it pays back for the human engineers as much as for the tooling.

Either as a fixed cost for an initial outcome, such as a first release or a defined migration, or as a monthly rate for the team once later phases are under way. We do not publish a figure for a build, because the scale is set by the number of features, platforms, user groups and users, and projects have run from four weeks to several months. The How We Charge page sets out both models, milestone billing and the notice period.

Typically one full-time engineer on the build, with two part-time roles alongside: one running the project and one working with you on priorities and technical decisions. Teams are shaped around the system rather than around one technology, so Python work draws in the neighbouring stack when it needs to. Delivery starts under Australian leadership, with the engineering in our Manila delivery centre.

Discovery establishes the scope, systems involved and delivery requirements. We agree the implementation plan and timing after that work, before implementation begins.