Python Development
Backends, APIs, data pipelines, and AI features in Python — built with Django and FastAPI by engineers who've run them in production.
Get a free quotePython earns its keep three ways: web backends that ship quickly, data pipelines that don't fall over, and AI work where the whole ecosystem lives anyway. At GTS Infosoft we build Django applications when you want batteries included — admin, auth, ORM out of the box — and FastAPI services when you need async speed and clean, typed APIs. Same language either way, which keeps your codebase and your hiring simple.
The AI angle matters more every year. RAG pipelines, OpenAI integrations, model serving, the pandas-shaped data work that feeds all of it — it's Python end to end, and it's what our team does daily. We're an ISO 9001:2015 certified studio in India, 16 years old, with dedicated Python teams from around USD 20/hr serving clients across India, the USA and Australia.
We've shipped OpenAI-backed features and RAG pipelines for real clients, not just demos
16 years in software, 250+ projects delivered across India, the USA and Australia
Our India-based rates start around USD 20/hr for dedicated developers. A typical Django or FastAPI backend for an MVP runs USD 6,000-20,000 depending on data models, integrations, and infrastructure needs. Describe what you're building and we'll send back a free, itemized quote.
Django when you want a full product fast: admin panel, auth, ORM, and migrations come free. FastAPI when you're building APIs or microservices and need async performance with typed contracts. We often pair them — Django for the main app, FastAPI for a high-traffic service beside it.
Yes — that's a growing share of our work. Chat assistants on OpenAI, retrieval-augmented search over your own documents, classification pipelines, model serving with FastAPI. We keep keys and prompts server-side and put cost controls in from day one, because LLM bills surprise people.
For most products, comfortably. Async FastAPI handles thousands of requests per second on modest hardware, and the usual bottleneck is the database, not the language. When a hot path genuinely needs more, we cache with Redis or move that one piece elsewhere — no need to abandon Python wholesale.
Often, yes. We start with an audit — dependency health, test coverage, Python version, security patches — and give you a straight assessment. Upgrading an old Django 2.x app or untangling an unmaintained Flask service is bread-and-butter work for us.
“I love working with Nav, he is always flexible and open to suggestions. Great delivery, great quality. Thanks again.”
Backend, data pipeline, or AI feature — describe it and our senior Python team will scope it for free.
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