This benchmark assesses the performance of fully-managed vector databases with typical workloads.

• For the setup, datasets, and detailed results of the benchmark, please visit https://myscale.github.io/benchmark.
• A summary of the benchmark results is available in our blog post.

Here's a preview of the results:

1. Queries Per Second (QPS): Higher QPS is preferable as it signifies greater throughput.
   • Throughput for Vector Search
   • Throughput for Filtered Vector Search
2. The cost-performance ratio is calculated by dividing the monthly cost by the QPS of the services per one hundred units. A lower ratio suggests better cost effectiveness.
   • Cost-performance ratio for Vector Search
   • Cost-performance ratio for Filtered Vector Search

First, install the necessary libraries on the client used for the benchmark.

pip install -r requirements.txt

Afterwards, follow the Step-by-Step Guide for Benchmark to execute the benchmark for each cloud service. You can refer to Results Visualization for visualizing the test results.

This repository is a fork of qdrant/vector-db-benchmark, specifically tailored for fully-managed vector databases.