Benchmarking
Recall, latency, and cost — with tables and charts.
The suite measures recall@k and latency against a labeled dataset and models cost versus Pinecone, OpenSearch, Qdrant, Weaviate, and Milvus across dimensions and scale.
pip install "dynavec[benchmark]"
# recall + latency (local control, or real AWS)
python -m benchmarks.run_benchmark --backend local --n 50000 --dim 384
python -m benchmarks.run_benchmark --backend dynavec --bucket my-vectors \
--index bench --table dynavec_bench --n 100000 --dim 1536
# cost comparison + charts (dimensions 384-3072, 100K -> 1B vectors)
python -m benchmarks.report --qpm 1_000_000
Cost figures are cost-model estimates from public list prices. Competitor recall/latency
are representative until you run the live benchmark against your own account.