Do I need low-latency vector search over 10 million embeddings?

Maybe — but 10 million embeddings alone doesn’t automatically mean you need low-latency vector search.

The real question is whether your application has tight response-time requirements and whether search happens interactively.

You likely need low-latency vector search if:

• Users expect results in under ~100 ms to a few hundred ms
• Search is part of a chatbot, recommendations, autocomplete, semantic search, or retrieval-augmented generation
• You’re doing many queries per second
• Re-ranking or downstream generation depends on fast retrieval
• You need a good user experience with “instant” results

You may not need it if:

• Searches are batch/offline
• A few seconds of delay is acceptable
• Queries are rare
• You can precompute results or use caching
• You mostly need analytics, deduplication, clustering, or periodic matching

For 10 million embeddings specifically:

• That’s a size where brute force search is usually too slow for interactive use
• You’ll typically want an approximate nearest neighbor (ANN) index
• Whether it must be “low-latency” depends on your SLA:
  • <50 ms: usually yes, you’ll need a well-tuned ANN setup
  • 50–200 ms: still likely yes, but easier
  • >500 ms or batch: maybe not necessary

Good rule of thumb

If the user is waiting directly on the result, you probably need low-latency vector search.
If the search is part of a background pipeline, you probably don’t.

To decide, answer these 4 questions:

1. How fast must a query return?
2. How many queries per second?
3. Do you need exact nearest neighbors or approximate is fine?
4. Is this user-facing or batch/offline?

If you want, I can help you determine this from your use case by estimating:

• expected latency,
• memory/index size,
• and whether a vector database, FAISS, Elasticsearch, or a custom ANN index makes sense.