



Open-source comparative benchmarks evaluating vector search performance of engines like Qdrant, Elasticsearch, Milvus, Redis, and Weaviate. Covers single-node upload/search, filtered search across various datasets and configurations, focusing on RPS, latency, precision, and indexing time using affordable hardware.
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Benchmarks compare relative performance rather than absolute numbers, run on identical affordable Azure machines (8 vCPU, 32GB RAM server), Dockerized with 25GB memory limit. Inspired by ann-benchmarks, tests multiple datasets with varying dimensions and distance metrics.
| Dataset | # Vectors | Dimensions | Distance |
|---|---|---|---|
| dbpedia-openai-1M-angular | 1M | 1536 | cosine |
| deep-image-96-angular | 10M | 96 | cosine |
| gist-960-euclidean | 1M | 960 | euclidean |
| glove-100-angular | 1.2M | 100 | cosine |
Qdrant avoids speed/accuracy pitfalls via advanced query planning. Others show downturns or collapses.
Fully open-sourced; see GitHub repository for setup.