Python Development Services

For most web workloads, yes, because database and network time dominate. Caching, async I/O, queues and horizontal scaling cover the common cases. For CPU-bound hot spots we use compiled libraries such as NumPy or move that piece to Rust or Go.

Choose Django for full applications with admin screens, authentication and relational data. Choose FastAPI for API-first services, machine-learning endpoints and async-heavy workloads. Some products use both, with Django for the core app and FastAPI for specialized services.

Yes. We usually add them as a separate module or service with its own tests, evaluation set and monitoring, then connect it through a clear interface. That limits risk to the existing product and lets you change models or providers later.

We pin versions with a lockfile using Poetry or uv, build reproducible Docker images, run the same checks in CI as locally, and scan for known vulnerabilities. Environments differ only through configuration, which removes the classic works-on-my-machine problem.