do i need a vector database for semantic search?

Not necessarily.

You need a way to compare embeddings quickly, but that does not always require a vector database.

When you do not need a vector DB

A vector database is often unnecessary if:

• your dataset is small or medium-sized
• you can load embeddings into memory
• you only need basic semantic search
• you already have a system that can do nearest-neighbor search, like:
  • FAISS
  • Annoy
  • ScaNN
  • even NumPy / cosine similarity for small datasets

When a vector DB is useful

A vector database helps when you need:

• large-scale search over many embeddings
• fast retrieval with indexing
• persistence of vectors
• metadata filtering alongside semantic search
• easy scaling and operations
• built-in features like:
  • upserts
  • deletion
  • hybrid search
  • replication / sharding

Rule of thumb

• < 10k–100k vectors: often no vector DB needed
• 100k+ vectors: vector DB or ANN index starts to make more sense
• millions of vectors: a vector DB is often the practical choice

Alternatives

You can also use:

• PostgreSQL + pgvector
• Elasticsearch / OpenSearch
• FAISS with your own storage layer

Bottom line

For semantic search, you need vector similarity search, but not always a vector database.
A vector DB is mainly a convenience and scaling tool.

If you want, I can help you choose the best option based on your dataset size and app requirements.