Python backends that handle real traffic, scale clean, and carry the AI features you will need next.

Yes — roughly 35% of our Python work is built for other agencies and consultancies under NDA. Three partnership models: white-label (your brand, our engineers, fully invisible), agency-of-record (a dedicated Python pod working as your in-house capacity), and capacity overflow (sprint-by-sprint engagement when your in-house team is full). Code ownership transfers to your repositories. Time zones overlap with the UK, EU, and US workday, and we run inside your tooling — Slack, Jira, Linear, ClickUp, Asana — as standard.

Our entire Python engineering team is based in Noida, India — 40 people in our iThum Tower B office, founded in 2015. We work with product teams and agencies across the UK, US, Ireland, Australia, the UAE, Germany, and the Netherlands. Working hours overlap with UK mornings, the full EU workday, and US afternoons. For agency partners we run in their tooling — Slack, Jira, Linear, ClickUp, Asana — as standard.

Django for whole products — apps with users, an admin, content, and a database at the centre, where the batteries-included framework saves months. FastAPI for APIs and services, async and I/O-bound workloads, and AI backends, where a lean typed framework fits better. They are not really competitors, and the architecture we ship most often uses both: Django for the product, FastAPI for the focused services that need to be async and fast. We will tell you on the discovery call which one — or which combination — fits your build.

Custom Python builds range from focused FastAPI services through to full Django SaaS products and AI backends. The right scope drivers are the number of features, the user roles and permissions, third-party integrations, whether AI or data work is involved, and the team’s Python maturity. We scope every engagement against the specific brief, are competitive with established engineering rates internationally, and are honest about which features can wait until phase two.

A typical Django SaaS application takes 10 to 16 weeks. A FastAPI service or API for a separate frontend takes 6 to 12 weeks. A multi-tenant Django platform with billing and admin takes 14 to 20 weeks. An AI or RAG backend takes 8 to 14 weeks depending on the retrieval and orchestration complexity. We work in two-week sprints with weekly demos and a deployable staging environment from sprint one.

Yes — every Python application we ship hits p95 API latency under 200 ms on real production traffic, query count under 10 per read request, and a cache hit ratio above 80% on cacheable endpoints. Python itself is rarely the bottleneck in a web request, because the request spends its time waiting on the database, not running Python. We achieve the targets through proper eager-loading, database indexing, Redis caching, task queues for slow work, async views where I/O fan-out justifies them, and a profiled production database. Performance is part of the build, not an afterthought.

Yes — every Python build we ship is typed end to end and checked with mypy in strict mode on every CI run. We use Pydantic for request and response validation at the edges of the system, type hints throughout the code, and we treat a type error as a build failure. Typed Python is what makes a codebase safe to refactor, and it is the foundation for reliable AI features, where the model’s output has to be parsed into a known shape before anything downstream trusts it.

Yes — and it is one of the main reasons teams come to us for Python. Most products will need an AI feature within the next year, and whether that is a clean addition or a painful rebuild depends on the backend underneath. We build Python backends that are typed, tested, and structured so that a retrieval-augmented feature, an AI workflow, or an LLM integration can be added as a service rather than retrofitted into a monolith. For the deeper version of that work, see our AI Engineering page.

All three, depending on what fits. Django for full products with an admin and an ORM at the centre. Django REST Framework when a Django product also needs a clean API for a mobile app or single-page frontend — it builds production APIs without leaving Django. FastAPI for standalone services, async and I/O-bound workloads, and AI backends. Many of our builds use more than one — a Django product with a FastAPI service alongside it for the AI work. We recommend the combination on the discovery call based on the product, not on fashion.

Yes. WCAG 2.2 AA is our baseline on every Python application’s frontend — colour contrast checked per token, full keyboard navigation, screen-reader landmarks audited, form labels reviewed, focus management on modals. We tighten the Django admin’s defaults for accessibility, and use accessible component primitives for any React or Vue frontend sitting on a FastAPI or Django REST Framework backend. Accessibility is part of the build, not a fix after launch.

Yes. We offer monthly Python maintenance retainers covering security patches, Python and Django version upgrades, dependency updates with pip-audit, database performance monitoring, task queue health monitoring, error tracking with Sentry, backup verification, and a fixed allocation of engineering hours per month. For teams without a dedicated Python engineer in-house, this is how the application stays healthy, secure, and fast past month one.

Yes. We regularly take over Python codebases — adding the tests, types, and observability they were missing — and run Django version upgrades, which are typically 4 to 10 week engagements depending on how far behind the codebase is. The process: audit the codebase for deprecated patterns and security gaps, add a test suite if one is missing, upgrade Django one major version at a time with tests passing at each step, update dependencies, then deploy to staging before production. We have done many of these, including Python 2 to 3 migrations on older systems.

Our default 2026 Python stack: Python 3.12; Django with Django REST Framework for product backends, or FastAPI for standalone and AI services; PostgreSQL with proper indexing, plus the pgvector extension for AI retrieval; Redis for cache, sessions, and as the Celery broker; Celery for background work; Pydantic for validation and mypy strict for type checking; pytest for tests; Sentry for error tracking; and structured logging with an observability tool. For AI backends we add the OpenAI and Anthropic SDKs behind a thin abstraction so models can be swapped. We adjust to the team and requirements, but that is what we recommend by default.

Both. The Python backend is the foundation, and we also build the AI features that run on it — retrieval-augmented generation, AI workflows, and in-product LLM features — with evaluations and observability so the AI is measured rather than hoped about. This Python and Django page covers the engineering foundation; our AI Engineering page covers the AI systems, the eval methodology, and the service-level objectives in depth. Many engagements start as a Python backend and grow into AI work once the foundation is solid.