Should I use a vector database if my app only needs nearest-neighbor search?

Maybe — but not necessarily.

If your app only needs nearest-neighbor search, a vector database is often more than you need. In many cases, a simpler option is better:

Use a vector database if you need:

• Approximate nearest-neighbor search at scale
• Fast updates/inserts alongside search
• Metadata filtering with vector search
• Persistence + replication + operational tooling
• Hybrid retrieval (vector + keyword, reranking, etc.)
• A managed service so you don’t have to build indexing/serving yourself

You may not need a vector database if:

• Your dataset is small to moderate
• You only need basic k-NN search
• Latency requirements are modest
• You can use an existing library or search engine

Simpler alternatives:

• In-memory libraries like:
  • FAISS
  • hnswlib
  • Annoy
• Traditional search engines with vector support:
  • Elasticsearch
  • OpenSearch
  • Postgres + pgvector if your scale is manageable

Rule of thumb:

• Prototype / small app: use a library or Postgres + pgvector
• Production with operational needs or larger scale: consider a vector database
• If you only want “find the closest vectors” and nothing else: a vector DB may be overkill

If you want, I can help you choose between FAISS, pgvector, and a vector database based on your expected dataset size, update rate, and latency target.