# Benchmarks

> Price, throughput, latency, and recall benchmarks against pgvector, pgvectorscale, and Elasticsearch.

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LLMS index: [llms.txt](/llms.txt)

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Section pages:

- [Price Benchmark](/docs/vchord/benchmark/price/): Using VectorChord, you can achieve a QPS of 131 with 0.95 precision on 100 million 768-dimensional vectors for the top 10 queries. This setup costs only $250 mo
- [Performance Benchmarking](/docs/vchord/benchmark/performance/): Here we present a comprehensive benchmark comparing our implementation of VectorChord with other vector databases, focusing on QPS and scalability.  If you are
- [Benchmark with pgvector](/docs/vchord/benchmark/pgvector/): In this section, we compare VectorChord with pgvector, a PostgreSQL extension that provides vector similarity search capabilities. We will focus on four key met
- [Benchmark with Elasticsearch](/docs/vchord/benchmark/elasticsearch/): Here we present a comprehensive benchmark comparing our implementation of VectorChord-BM25 with ElasticSearch, focusing on two key metrics: QPS (Queries Per Sec
- [Benchmark with pgvectorscale](/docs/vchord/benchmark/pgvectorscale/): In this section, we compare VectorChord with pgvectorscale, a PostgreSQL extension that provides vector similarity search capabilities. We will focus on four ke