Just put Zvec vs LanceDB vs Qdrant through the paces on a 3 collection (text only) 10k per collection dataset.
Average latency across ~500 queries per collection per database:
Qdrant: 21.1ms
LanceDB: 5.9ms
Zvec: 0.8ms
Both Qdrant and LanceDB are running with Inverse Document Frequency enabled so that is a slight performance hit, Zvec running with HNSW.
Overlap of answers between the 3 is virtually identical with same default ranking.
So yes, Zvec is incredible, but the gotcha is that the reason zvec is fast is because it is primarily constrained by local disk performance and the data must be local disk, meaning you may have a central repository storing the data, but every instance running zvec needs to have a local (high perf) disk attached. I mounted blobfuse2 object storage to test and zvec numbers went to over 100ms, so disk is almost all that matters.
My take? Right now the way zvec behaves, it will be amazing for on-device vector lookups, not as helpful for cloud vectors.
Average latency across ~500 queries per collection per database:
Qdrant: 21.1ms LanceDB: 5.9ms Zvec: 0.8ms
Both Qdrant and LanceDB are running with Inverse Document Frequency enabled so that is a slight performance hit, Zvec running with HNSW.
Overlap of answers between the 3 is virtually identical with same default ranking.
So yes, Zvec is incredible, but the gotcha is that the reason zvec is fast is because it is primarily constrained by local disk performance and the data must be local disk, meaning you may have a central repository storing the data, but every instance running zvec needs to have a local (high perf) disk attached. I mounted blobfuse2 object storage to test and zvec numbers went to over 100ms, so disk is almost all that matters.
My take? Right now the way zvec behaves, it will be amazing for on-device vector lookups, not as helpful for cloud vectors.