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A benchmarking resource for evaluating approximate nearest neighbor search (ANNS) methods on billion-scale datasets, highly relevant for assessing the scalability of vector databases.
A set of benchmarks provided by Qdrant for evaluating vector databases, focusing on speed, scalability, and accuracy of vector search operations.
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.
Benchmark results and tools by MyScale aimed at measuring the performance of vector databases in various search and retrieval tasks.
VectorDBBench is a benchmarking tool developed by ZillizTech for evaluating the performance of various vector databases, aiding users in selecting suitable vector database solutions for their needs.
A unified system designed for efficient multi-index vector search, directly addressing large-scale vector database performance and scalability challenges.
A benchmarking framework for evaluating approximate nearest neighbor search (ANNS) algorithms on billion-scale datasets. It is designed to assess the scalability and performance of vector databases and ANNS methods on very large datasets.
benchmark, anns, scalability, performance
https://github.com/harsha-simhadri/big-ann-benchmarks