Do I need a vector database if my documents are small?

Not necessarily.

If your document set is small, you can often skip a vector database and use simpler options:

• Plain keyword search: great if the text is small and queries are straightforward.
• In-memory embeddings + linear scan: fine for a few hundred to a few thousand chunks.
• SQL with vector extensions: good middle ground if you already use Postgres, SQLite, etc.
• Simple file-based index: can work for prototypes or local apps.

When you probably don’t need a vector DB

• You have few documents.
• You can fit all embeddings in memory.
• Latency isn’t critical.
• You don’t need advanced filtering, scaling, or multi-user access.

When a vector DB starts to help

• You have many chunks or expect growth.
• You need fast semantic search at scale.
• You want metadata filtering plus vector similarity.
• You need persistence, updates, concurrency, or production reliability.

Rule of thumb

If your corpus is small enough that you can:

1. embed all chunks,
2. keep them in memory or in a regular database,
3. and search them quickly,

then a dedicated vector database is often overkill.

If you want, I can suggest the simplest setup based on your document count and app stack.