Should I use Azure AI Search for private document Q&A?

Yes—Azure AI Search is a strong choice for private document Q&A if you want a managed, enterprise-friendly retrieval layer for an LLM app.

Good fit when you want:

• Private data access: keep documents in your own Azure environment
• Hybrid search: keyword + semantic + vector retrieval
• Security controls: role-based access, network isolation, encryption
• Scalability: handle many documents and queries without building search infra yourself
• RAG support: a solid retrieval backend for retrieval-augmented generation

It’s especially useful if:

• Your docs are in PDFs, Office files, SharePoint, Blob Storage, etc.
• You need citations / source grounding
• You want to index large corpora and support permission-aware search
• You’re already on Azure or need enterprise compliance features

Watch out for:

• Cost: can get expensive as indexes, queries, and semantic/vector features grow
• Ingestion complexity: you still need a pipeline to chunk, embed, and index content
• Quality tuning: good Q&A depends heavily on chunking, embedding model, and prompts—not just search
• Permission trimming: if different users should see different docs, you must implement access filtering carefully

Alternatives to consider:

• Postgres + pgvector if your corpus is smaller and you want a simpler stack
• Pinecone / Weaviate / Milvus if you want a vector-first system
• Elastic if you already use it and want strong text search
• Built-in retrieval in your app framework if the corpus is small and requirements are simple

Rule of thumb

Use Azure AI Search if you need a production-grade, secure, Azure-native retrieval system for private documents.
Skip it if your use case is small, budget-sensitive, or you want the simplest possible stack.

If you want, I can also give you a recommended architecture for private document Q&A on Azure or help you decide between Azure AI Search vs pgvector vs Pinecone.