Do I need a vector database for semantic search?

Not necessarily.

You need a way to do similarity search over embeddings. A vector database is one common way to do that, but it’s not always required.

You may not need a vector DB if:

• Your dataset is small.
• You can load embeddings into memory and do brute-force nearest-neighbor search.
• You already use a database that supports vectors, like:
  • Postgres + pgvector
  • SQLite + vector extensions
  • OpenSearch / Elasticsearch vector search
  • MongoDB Atlas Vector Search

A vector database is useful if:

• You have lots of embeddings.
• You need fast low-latency search at scale.
• You want indexing, filtering, metadata support, and operational tooling.
• You expect the corpus to grow continuously.

Rule of thumb

• Prototype / small app: no vector DB needed.
• Production / large corpus / performance constraints: a vector DB or vector-capable search system is often a good idea.

Also important

Semantic search is more than just storing vectors:

1. Convert text to embeddings
2. Store embeddings somewhere
3. Retrieve nearest neighbors
4. Optionally re-rank results with a stronger model

If you want, I can suggest the best setup based on your expected data size and stack.