Benchmarks

See numbers from LanceDB Enterprise’s performance scalability and latency benchmarks.

LanceDB’s architecture is designed to deliver 25ms vector search latency. Even with metadata filtering, our query latency remains as low as 50ms. It is important to note that we can support thousands of QPS with such query performance.

Percentile	Vector Search	Vector Search w. Filtering	Full-Text Search
P50	25ms	30ms	26ms
P90	26ms	39ms	37ms
P99	35ms	50ms	42ms

Dataset

We used two datasets for this benchmark test: the dbpedia-entities-openai-1M for vector search, and a synthetic dataset for vector search with metadata filtering.

Name	# Vectors	Vector Dimension
dbpedia-entities-openai-1M	1,000,000	1536
synthetic dataset	15,000,000	256

Vector Search

We ran vector queries with dbpedia-entities-openai-1M with a warmed-up cache. The query latency is as follows:

Percentile	Latency
P50	25ms
P90	26ms
P99	35ms
Max	49ms

Full-Text Search

With the same dataset and a warmed-up cache, the full-text search performance is as follows:

Percentile	Latency
P50	26ms
P90	37ms
P99	42ms
Max	98ms

Vector Search with Metadata Filtering

We created a 15M-vector dataset with sufficient complexity to thoroughly test our complex metadata filtering capabilities. Such filtering can span a wide range of scalar columns, e.g., “find Sci-fi movies since 1900”.

With a warmed-up cache, the query performance using slightly more selective filters, e.g., “find Sci-fi movies between the years 2000 and 2012”, is as follows:

Percentile	Latency
P50	30ms
P90	39ms
P99	50ms

The query performance using complex filters, e.g., “find Sci-fi movies since 1900”, is as follows:

Percentile	Latency
P50	65ms
P90	76ms
P99	100ms

Our benchmarks are designed to provide consistent and reproducible performance evaluations of LanceDB. We regularly update and re-run these benchmarks to ensure the data remains accurate and relevant.