Qdrant vs High-Throughput Vector DBs at 10M+ Vectors: How to Choose?
InsideDebt6345 · reddit · 2026-08-12
The author discusses the trade-off between recall and throughput in vector databases at a scale of over 10 million vectors.
Citing a benchmark (768 dimensions, same hardware), Qdrant achieved a high recall of 0.9985 at 23 QPS, whereas VectorAI DB hit 745 QPS with a slightly lower recall of 0.9882.
For most RAG systems, the author prefers Qdrant for its higher recall and simpler operations. However, they ask the community for a rule of thumb when dealing with massive scale and high query volumes: should one sacrifice some recall for massive throughput gains, or scale out a high-recall database like Qdrant using replicas?
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