# ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithms

arXiv: https://arxiv.org/abs/1807.05614 · HTML: https://mlsanity.com/paper/1807.05614/

## Key facts

- Cited by 130 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2018.

## Built on by (most cited first, 30 of 130)

- [The Faiss library](https://mlsanity.com/paper/2401.08281/) (2024)
- [Results of the NeurIPS'21 Challenge on Billion-Scale Approximate Nearest Neighbor Search](https://mlsanity.com/paper/2205.03763/) (2022)
- [Neural Approaches to Conversational Information Retrieval](https://mlsanity.com/paper/2201.05176/) (2022)
- [Online Continual Learning Without the Storage Constraint](https://mlsanity.com/paper/2305.09253/) (2023)
- [PatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERT](https://mlsanity.com/paper/2103.11933/) (2021)
- [ParlayANN: Scalable and Deterministic Parallel Graph-Based Approximate Nearest Neighbor Search Algorithms](https://mlsanity.com/paper/2305.04359/) (2023)
- [CAGRA: Highly Parallel Graph Construction and Approximate Nearest Neighbor Search for GPUs](https://mlsanity.com/paper/2308.15136/) (2023)
- [Results of the Big ANN: NeurIPS'23 competition](https://mlsanity.com/paper/2409.17424/) (2024)
- [Similarity search in the blink of an eye with compressed indices](https://mlsanity.com/paper/2304.04759/) (2023)
- [TPU-KNN: K Nearest Neighbor Search at Peak FLOP/s](https://mlsanity.com/paper/2206.14286/) (2022)
- [Back to the Basics: Revisiting Out-of-Distribution Detection Baselines](https://mlsanity.com/paper/2207.03061/) (2022)
- [OOD-DiskANN: Efficient and Scalable Graph ANNS for Out-of-Distribution Queries](https://mlsanity.com/paper/2211.12850/) (2022)
- [Accelerating Retrieval-Augmented Generation](https://mlsanity.com/paper/2412.15246/) (2024)
- [Specialized Document Embeddings for Aspect-based Similarity of Research Papers](https://mlsanity.com/paper/2203.14541/) (2022)
- [SOAR: Improved Indexing for Approximate Nearest Neighbor Search](https://mlsanity.com/paper/2404.00774/) (2024)
- [Approximate Nearest Neighbor Search with Window Filters](https://mlsanity.com/paper/2402.00943/) (2024)
- [Locally-Adaptive Quantization for Streaming Vector Search](https://mlsanity.com/paper/2402.02044/) (2024)
- [MobileRAG: A Fast, Memory-Efficient, and Energy-Efficient Method for On-Device RAG](https://mlsanity.com/paper/2507.01079/) (2025)
- [A Theoretical Analysis Of Nearest Neighbor Search On Approximate Near Neighbor Graph](https://mlsanity.com/paper/2303.06210/) (2023)
- [DEANN: Speeding up Kernel-Density Estimation using Approximate Nearest Neighbor Search](https://mlsanity.com/paper/2107.02736/) (2021)
- [Single-Photon Structured Light](https://mlsanity.com/paper/2204.05300/) (2022)
- [IceFormer: Accelerated Inference with Long-Sequence Transformers on CPUs](https://mlsanity.com/paper/2405.02842/) (2024)
- [PDX: A Data Layout for Vector Similarity Search](https://mlsanity.com/paper/2503.04422/) (2025)
- [Automating Nearest Neighbor Search Configuration with Constrained Optimization](https://mlsanity.com/paper/2301.01702/) (2023)
- [Falconn++: A Locality-sensitive Filtering Approach for Approximate Nearest Neighbor Search](https://mlsanity.com/paper/2206.01382/) (2022)
- [VIBE: Vector Index Benchmark for Embeddings](https://mlsanity.com/paper/2505.17810/) (2025)
- [Improving Zero-Shot Text Matching for Financial Auditing with Large Language Models](https://mlsanity.com/paper/2308.06111/) (2023)
- [LeanVec: Searching vectors faster by making them fit](https://mlsanity.com/paper/2312.16335/) (2023)
- [A Fast Partial Video Copy Detection Using KNN and Global Feature Database](https://mlsanity.com/paper/2105.01713/) (2021)
- [Business Entity Matching with Siamese Graph Convolutional Networks](https://mlsanity.com/paper/2105.03701/) (2021)

## Abstract (arXiv)

This paper describes ANN-Benchmarks, a tool for evaluating the performance of in-memory approximate nearest neighbor algorithms. It provides a standard interface for measuring the performance and quality achieved by nearest neighbor algorithms on different standard data sets. It supports several different ways of integrating k-NN algorithms, and its configuration system automatically tests a range of parameter settings for each algorithm. Algorithms are compared with respect to many different (approximate) quality measures, and adding more is easy and fast; the included plotting front-ends can visualise these as images, LaTeX plots, and websites with interactive plots. ANN-Benchmarks aims to provide a constantly updated overview of the current state of the art of k-NN algorithms. In the short term, this overview allows users to choose the correct k-NN algorithm and parameters for their similarity search task; in the longer term, algorithm designers will be able to use this overview to test and refine automatic parameter tuning. The paper gives an overview of the system, evaluates the results of the benchmark, and points out directions for future work. Interestingly, very different approaches to k-NN search yield comparable quality-performance trade-offs. The system is available at http://ann-benchmarks.com .