The Excellence
Directory Platform Template

Open-source distributed graph database designed for super large-scale graphs with billions of vertices and trillions of edges. Outperforms Neo4j on larger datasets while providing graph database capabilities for AI applications.

Unified search and AI engine with seamless scaling, intelligent retrieval, and precision ranking. Goes beyond simple vector search with tensor support, multi-phase ranking, and hybrid retrieval blending semantic, textual, and structured signals at scale.

Milvus Distributed is a horizontally scalable, distributed deployment of the Milvus vector database designed for enterprise workloads, offering high reliability and the ability to handle billions of vectors with a comprehensive management toolkit.

ParlayANN is a scalable and deterministic parallel graph-based approximate nearest neighbor (ANN) search library. It provides parallel algorithms and implementations for high-dimensional vector similarity search, suitable as a core search component in large-scale vector database and retrieval systems.

Apache Cassandra is a distributed NoSQL database that is adding native support for high-dimensional vector storage and approximate nearest neighbor search, making it a scalable choice for AI and vector search workloads.

AstraDB (also known as Astra DB by DataStax) is a cloud-native vector database built on Apache Cassandra, supporting real-time AI applications with scalable vector search. It is designed for large-scale deployments and features a user-friendly Data API, robust vector capabilities, and automation for AI-powered applications.

A distributed vector database designed for scalable and efficient vector similarity search. It is purpose-built for handling large-scale vector data and search workloads.

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.

Elasticsearch is a distributed search engine supporting various data types, including vectors, and provides scalable vector search capabilities, making it a popular choice for modern AI-powered applications. It can be extended with the k-NN plugin to provide scalable vector search using HNSW and Lucene, enabling hybrid semantic and keyword search capabilities.

Epsilla is an open-source vector database optimized for high-performance similarity search and scalable storage of vector embeddings.

FAISS (Facebook AI Similarity Search) is a popular open-source library for efficient similarity search and clustering of dense vectors. Developed by Facebook/Meta, it supports billions of vectors and is widely used to power vector search engines and databases, especially where raw speed and scalability are needed.

KDB is a high-performance vector database supporting billion-scale vector search, with features aimed at enterprises needing large-scale vector storage and retrieval.