Can Chroma DB support sub-second latency for real-time AI voice agents?

All answers to this question.

For 100,000 vectors, you can absolutely stay under 100ms with Chroma, provided you keep the index warm. The first query might be slow as it loads the HNSW index from disk, but subsequent queries will be lightning-fast because Chroma keeps the hot parts of the index in the system's page cache. In a test I ran in November 2024 on a standard Ubuntu server, our p95 latency for a 100k collection was consistently around 12-15ms. The real bottleneck for your voice agent will likely be the LLM generation time or the TTS, not the Chroma DB retrieval.

   Answered 2025-10-15 by Kathleen Scott

Should I use a specific distance metric like Cosine or L2 to get the fastest possible response time?

   Answered 2025-10-17 by Brian King

• In terms of raw speed, the difference between Cosine and L2 is negligible in Chroma because they are both highly optimized C++ implementations. You should choose the metric that matches how your embedding model was trained. Most modern models like OpenAI’s text-embedding-3-small work best with Cosine similarity. I found that focus on "dimensionality reduction" (using smaller embedding sizes) had a much bigger impact on my voice bot's speed than the choice of distance metric. Reducing from 1536 to 512 dimensions cut our search time by almost 40% in a project I did in late 2025.

     Commented 2025-10-19 by Gary Taylor

If speed is your goal, make sure your server has enough RAM to fit the entire index. That avoids disk hits entirely during the search.

   Answered 2025-10-20 by Larry Roberts

• Spot on, Larry. For 100k vectors at 1536 dimensions, you only need about 1-2GB of RAM to keep the whole thing in memory. That makes Chroma DB incredibly performant for real-time needs.

     Commented 2025-10-22 by Rebecca Young