Benchmarking Vector Search Libraries: Speed, Memory, and Accuracy Across 500–1M Samples
Benchmark vector search libraries by running the provided scripts on your dataset sizes to identify the fastest and most memory‑efficient option.
Run the benchmark scripts on your own data to determine which library best fits your performance and memory constraints.
Summary
A new benchmark compares popular vector search libraries—faiss, Scann, and Usearch—across dataset sizes ranging from 500 to 1 million samples. The study measures speed, memory usage, and similarity accuracy, providing a comprehensive view of each library’s trade‑offs. Results are published at https://mohamed-em2m.github.io/vector-search-benchmarks/ and the benchmark code is available on GitHub at https://github.com/mohamed-em2m/vector-search-benchmarks. The scripts allow users to register additional libraries, making the benchmark extensible. The comparison highlights which libraries excel in speed versus memory efficiency, helping developers choose the right tool for their workload. The project is written for Python 3.12+ and includes detailed instructions for running the tests. By running the benchmark on their own data, teams can quickly identify the most suitable library for their use case.
Key changes
- Compared faiss, Scann, Usearch across 500–1M samples
- Measured speed, memory usage, and similarity accuracy
- Results published at https://mohamed-em2m.github.io/vector-search-benchmarks/
- Code available at https://github.com/mohamed-em2m/vector-search-benchmarks
- Allows registration of additional libraries
- Highlights trade‑offs between speed and memory
- Supports Python 3.12+ environment
- Provides quick validation of library suitability