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LanceDB Enterprise is designed for low-latency, high-throughput search, but observed performance depends on factors such as dataset shape, index configuration, cache warmth, filter selectivity, concurrency, and cluster sizing. The figures on this page should be read as representative benchmark results for a specific test setup, not as universal guarantees for every deployment. In our benchmark environment, warmed-cache vector search reached around 25ms P50 latency, and metadata-filtered search reached around 50ms P99 latency for the filter pattern shown below.
If you want performance guidance for your own workload, reach out to contact@lancedb.com. The LanceDB engineering team can help map your latency, throughput, ingestion, and filtering requirements to an appropriate Enterprise cluster design.
PercentileVector SearchVector Search w. FilteringFull-Text Search
P5025ms30ms26ms
P9026ms39ms37ms
P9935ms50ms42ms
Depending on workload and tuning, Enterprise clusters can also be configured for high concurrency, including thousands of QPS in some deployments, but the right configuration varies by use case. Training, search, and analytics workloads often benefit from different cluster shapes and resource allocation strategies. To understand which parts of the system influence these results, see the Enterprise architecture guide.

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Dataset

We used two datasets for this benchmark: the dbpedia-entities-openai-1M for vector search, and a synthetic dataset for vector search with metadata filtering.
Name# VectorsVector Dimension
dbpedia-entities-openai-1M1,000,0001536
synthetic dataset15,000,000256
These benchmark results are most useful as a directional baseline. Different data distributions, index choices, cache behavior, and cluster settings can materially change the latency profile. We ran vector queries against dbpedia-entities-openai-1M with a warmed-up cache. In that benchmark setup, we observed the following latency profile:
PercentileLatency
P5025ms
P9026ms
P9935ms
Max49ms

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Full-Text Search

With the same dataset and a warmed-up cache, full-text search fell into the following range:
PercentileLatency
P5026ms
P9037ms
P9942ms
Max98ms

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Vector Search with Metadata Filtering

We created a 15M-vector dataset to evaluate metadata-aware search under more complex filtering conditions. These filters can span a wide range of scalar columns, for example, “find Sci-fi movies since 1900”. With a warmed-up cache, slightly more selective filters, for example, “find Sci-fi movies between the years 2000 and 2012”, produced the following representative results:
PercentileLatency
P5030ms
P9039ms
P9950ms
Broader or less selective filters, for example, “find Sci-fi movies since 1900”, moved the latency range to:
PercentileLatency
P5065ms
P9076ms
P99100ms
These benchmarks are intended to provide consistent, reproducible reference points for LanceDB Enterprise rather than one-size-fits-all promises. We periodically re-run and update numbers as necessary, but production performance will still depend on workload shape, cluster tuning, and the architectural choices described in the Enterprise architecture guide.