Python development for backend, automation and AI.

Python shines in three scenarios: high-concurrency backend (FastAPI supports tens of thousands of requests per second), process automation (data ingestion, workflows, ETLs) and everything related to artificial intelligence (LangChain, OpenAI SDK, HuggingFace, scikit-learn). If your project combines two or three of these cases, Python is probably the most efficient choice.

FastAPI for high-performance APIs with automatic documentation (OpenAPI), projects where speed and strict typing with Pydantic are priorities. Django for projects needing a robust ORM, integrated admin panel and mature plugin ecosystem. In enterprise projects with microservices we use FastAPI; for SaaS platforms with heavy business logic, Django remains the most productive choice.

Yes, and it's the most common pattern in our projects. The Python backend exposes a REST or GraphQL API consumed by the mobile app — regardless of whether it's in Flutter, React Native, native iOS or Android Kotlin. Python handles business logic, data and AI models; the mobile frontend only consumes and displays.

It depends on complexity: a basic FastAPI backend with authentication, CRUD and cloud deployment can be ready in 4–6 weeks. A system with integrated AI, data pipelines and high availability requires 3–6 months. The most honest way to know is starting with our discovery process — in 2 weeks you get a technical scope, a budget and a realistic plan.