# Source: https://docs.lancedb.com/enterprise/benchmarks.md

> ## Documentation Index
> Fetch the complete documentation index at: https://docs.lancedb.com/llms.txt
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# 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](https://huggingface.co/datasets/KShivendu/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  |

<Check>
  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.
</Check>