Python Development Company

Django is best for full-featured web apps that need an admin panel, ORM, user management, and template rendering. FastAPI is better for pure API backends where async performance matters, especially when building microservices or AI inference endpoints. We often use both in the same system.

Yes. We integrate OpenAI, Claude, and open-source models into existing Django and FastAPI applications. This includes setting up inference endpoints, building RAG pipelines with vector databases, and optimizing for latency and cost. Most integrations take 2-4 weeks.

Python with FastAPI handles 3K-5K requests/second on typical hardware, which is sufficient for most applications. For CPU-heavy tasks, we use multiprocessing or offload to compiled extensions. For extreme throughput requirements (50K+ req/sec), we recommend Go or Rust for the hot path.

Cost follows complexity. A Django web app with authentication, an admin panel, and a basic API is the most predictable tier and scopes lower. AI-integrated applications that add custom ML pipelines, model inference, and evaluation involve more specialized engineering, so they scope higher. Data engineering projects with Airflow pipelines depend on data volume and the number of sources you connect. The honest answer is that we scope it on a free call and give you a firm figure before development begins.

Yes. We containerize Python apps with Docker, wire up CI/CD on GitHub Actions, deploy to AWS ECS or Kubernetes, tune the gunicorn and uvicorn workers, and put monitoring in place with Sentry and Prometheus. Every project leaves with a deployment pipeline that is ready for real traffic, not just a local demo.

We size a dedicated Python team to the seniority and scope your product needs. If the work leans heavy on AI or ML, that experience is already priced in rather than tacked on later. Every seat is full-time at 160 hours a month, with equipment and management handled on our side. One call and you have the figure.

Node.js is 10-15% cheaper for standard web APIs because of TypeScript type sharing with the frontend. Python is more cost-effective for AI/ML projects because its ML libraries (PyTorch, scikit-learn, LangChain) save weeks of custom development. For an AI-integrated SaaS, choosing Python over a hand-built ML stack can take weeks of pipeline work off the timeline. We scope the exact difference on a free call.

Yes. One we are proud of: an AI hiring platform that screens candidates automatically, built on Python, FastAPI, and LLM integration. With custom AI work, two things drive the cost above everything else, how complex the model gets and how much data it has to handle. We scope around those, put a dedicated Python AI team on it, and can start within days of the call.