Back-end technology
Python Development Services
Python is our default for data-heavy backends, APIs and automation. We build with FastAPI or Django, type-annotated and tested, and we know how to take Python services to production and keep them there.
- FastAPI & Django
- Typed & tested
- Async where it helps
- Data & AI ready
Why Python
Why teams choose Python
Fast to build, clear to read
Python's readability keeps delivery quick and onboarding painless.
The language of data and AI
The same stack that serves your API also runs your models, pipelines and analytics.
Mature web frameworks
Django for batteries-included products, FastAPI for high-throughput typed APIs.
A huge standard library and ecosystem
Whatever the integration, there is a well-maintained package for it.
Use cases
What we build with Python
REST and GraphQL APIs
Typed, documented services that other teams and mobile apps consume.
Data pipelines and ETL
Scheduled and streaming jobs that move and reshape data reliably.
AI and ML features
Retrieval systems, model serving and evaluation next to the product code.
Internal platforms and admin
Django admin and custom tooling that ops teams actually like.
How we work
How we build Python products
The same delivery discipline on every engagement, whatever the stack.
Discovery before code
A short kickoff to agree scope, architecture and the riskiest assumptions, so the first sprint moves in the right direction.
Trunk-based delivery
Small pull requests reviewed within hours, merged behind feature flags, and deployed continuously.
Tests that pay for themselves
Unit tests for logic, integration tests for the seams, and a thin layer of end-to-end tests on the paths that matter.
Observability from day one
Structured logs, traces and dashboards in place before launch, not after the first incident.
Documented and handed over
Architecture decision records, a running runbook, and a codebase your own team can own the day we leave.
Stack
The Python stack we reach for
Sensible defaults, not a fixed menu. We adapt to what your team already runs.
- FastAPI
- Django
- Pydantic
- SQLAlchemy
- PostgreSQL
- Celery
- Redis
- pytest
- Ruff
- mypy
- Docker
Related
Related technologies
Back-end
Node.js
TypeScript backends that share a language with your front end: NestJS or Fastify, run with real operational discipline.
Back-end
Java
The backends that cannot go down: Spring Boot on modern Java, with the test coverage and observability to operate them.
Back-end
Ruby on Rails
One of the fastest ways to a real product: modern Rails with Hotwire, solid test coverage and a clear path to scale.
FAQ
Python FAQ
Django or FastAPI?
Django when you want an admin, ORM, auth and templating out of the box. FastAPI when the service is API-only and throughput or typing is the priority. They also coexist well.
Is Python fast enough for production?
For the vast majority of web workloads, yes, especially with async frameworks and proper caching. Where a hot path needs more, we profile and optimize, or move that piece to a faster runtime.
Do you type-annotate Python?
Yes. We use type hints everywhere, check them with mypy in CI, and validate data at the edges with Pydantic.
Can Python serve our AI models?
That is one of its strengths. We serve models behind FastAPI, add evaluation and guardrails, and keep inference observable.
Ready to build with Python?
Tell us what you're working on. We come back within two business days with a point of view and next steps.