Django vs FastAPI for building a Retrieval-Augmented Generation (RAG) system [closed]
I want to start building a Retrieval-Augmented Generation (RAG) system that can answer questions based on custom data (for example documents, PDFs, or internal knowledge bases).
My current backend experience is mainly with Django and FastAPI. I have built REST APIs using both frameworks.
For a RAG architecture, I plan to use components like:
- Vector databases (such as Pinecone, Weaviate, or FAISS)
- Embedding models
- LLM APIs
- Libraries like LangChain or LlamaIndex
My main confusion is around the backend framework choice.
Questions:
1. Is FastAPI generally preferred over Django for building RAG-based APIs or AI microservices?
2. Are there any architectural advantages of using FastAPI for LLM pipelines and vector search workflows?
3. In what scenarios would Django still be a better choice for an AI/RAG system?
4. Are there any recommended project structures or best practices when integrating RAG pipelines with Python web frameworks?
I am trying to understand which framework would scale better and integrate more naturally with modern AI tooling.