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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.
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.
A benchmarking resource for evaluating approximate nearest neighbor search (ANNS) methods on billion-scale datasets, highly relevant for assessing the scalability of vector databases.
Milvus Sizing Tool helps users estimate the hardware and resource requirements needed to deploy Milvus based on their anticipated data scale and workload.
Benchmark results and tools by MyScale aimed at measuring the performance of vector databases in various search and retrieval tasks.
A set of benchmarks provided by Qdrant for evaluating vector databases, focusing on speed, scalability, and accuracy of vector search operations.
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 2024 paper introducing CANDY, a benchmark for continuous ANN search with a focus on dynamic data ingestion, crucial for next-generation vector databases.
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