Backend

FastAPI

FastAPI is my default Python backend for AI products: async endpoints, typed request models, and generated docs.

How I use it

I use FastAPI as the service layer in front of models and LLM pipelines. It handles streaming responses, WebSockets and background jobs well, and the generated OpenAPI docs mean a frontend team can integrate without asking me.

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Questions clients ask about FastAPI

Can FastAPI handle real-time features?
Yes. Dubbix uses FastAPI with WebSockets for live audio and video processing status. Long-running model calls go to background workers so requests do not block.
FastAPI or Node.js for my backend?
If the backend calls models or does heavy data work, FastAPI, because the ML ecosystem is Python. If it is mostly CRUD and your team is JavaScript, Node.js. I build both.

Work with me

Need FastAPI work done?

I work remotely with clients across the US, Canada, the UK, the UAE, Brazil and Australia. Tell me what you are building and I will reply with questions, a plan and an estimate.

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