Should I use a vector database or plain search for SharePoint content?

It depends on what you want to do with the SharePoint content.

Use plain search if you need:

• Exact keyword / phrase matching
• Filtering by metadata like author, date, site, document type
• Simple, fast, low-cost retrieval
• Search that users already expect from SharePoint or Microsoft Search
• Compliance-friendly, deterministic results

This is usually the best default for SharePoint content.

Use a vector database if you need:

• Semantic search: “find docs about onboarding” even if those words aren’t in the file
• Question answering / RAG over documents
• Similarity search across lots of unstructured content
• Cross-document retrieval where phrasing varies a lot

A vector DB is useful when the user’s query may not match the document wording.

Best practice for SharePoint in many cases: hybrid search

For SharePoint content, the strongest setup is often:

• Plain search / keyword search for precision and metadata
• Vector search for meaning and relevance
• Optionally combine them in a hybrid ranking system

This gives better results than either alone.

My recommendation

• If you just need users to find files and pages: use plain search
• If you’re building an AI assistant, semantic retrieval, or Q&A over SharePoint: use a vector DB
• If you can support it: use both

Important consideration

If your SharePoint data includes:

• permissions-sensitive content,
• compliance requirements,
• frequent updates,
• or you need source-of-truth accuracy,

then plain search or a hybrid system with strong permission filtering is usually safer than vector-only retrieval.

If you want, I can help you choose based on your specific use case, like:

• enterprise search
• Copilot-style Q&A
• document discovery
• hybrid architecture for SharePoint Online