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Amazon OpenSearch's k-NN plugin enables scalable, efficient vector search using ANN algorithms (IVF, HNSW) directly within a managed OpenSearch cluster. It is directly relevant for building, querying, and scaling vector databases on AWS.
An open-source library for approximate nearest neighbor search in high-dimensional spaces, often used as a backend for vector databases and search engines.
DiskANN is a graph-based approximate nearest neighbor search (ANNS) system optimized for fast and accurate billion-point nearest neighbor search on a single node, leveraging SSD storage. It is highly relevant for large-scale vector database applications requiring efficient vector search at scale.
HNSWLIB is a C++ library with Python bindings for fast approximate nearest neighbor search using Hierarchical Navigable Small World (HNSW) graphs, commonly used in vector database backends.
NVIDIA CAGRA is a GPU-accelerated graph-based library for approximate nearest neighbor searches, optimized for high-performance vector search leveraging modern GPU parallelism. It is suitable for scenarios requiring rapid, large-scale vector retrieval.
A library by Google Research for efficient vector similarity search, suitable for large-scale nearest neighbor applications in AI.
Category: SDKs & Libraries
Tags: vector-search, ann, managed-service, opensearch
Amazon OpenSearch's k-NN plugin enables scalable and efficient vector search using approximate nearest neighbor (ANN) algorithms such as IVF and HNSW directly within a managed OpenSearch cluster. This is particularly useful for building, querying, and scaling vector databases on AWS.
No pricing information is provided in the source content. For details about costs, refer to AWS OpenSearch managed service pricing.