The open‑source repository containing the implementation, configuration, and scripts of VectorDBBench, enabling users to run standardized benchmarks across multiple vector database systems locally or in CI.

WEAVESS is an open-source benchmarking and evaluation framework for graph-based approximate nearest neighbor (ANN) search methods, providing code and experiments for large-scale vector similarity search. It is useful for researchers and practitioners comparing vector indexing algorithms for vector databases and AI search applications.

BEIR (Benchmarking IR) is a benchmark suite for evaluating information retrieval and vector search systems across multiple tasks and datasets. Useful for comparing vector database performance.

ANN-Benchmarks is a benchmarking platform specifically for evaluating the performance of approximate nearest neighbor (ANN) search algorithms, which are foundational to vector database evaluation and comparison.

A massive text embedding benchmark for evaluating the quality of text embedding models, crucial for vector database applications.

A collection of datasets curated by Intel Labs specifically for evaluating and benchmarking vector search algorithms and databases.