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KDB is a high-performance vector database supporting billion-scale vector search, with features aimed at enterprises needing large-scale vector storage and retrieval.
Microsoft Azure offers vector search support across multiple database services, enabling developers to leverage vector search in cloud-native and enterprise scenarios.
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.
AWS has introduced vector search in several of its managed database services, including OpenSearch, Bedrock, MemoryDB, Neptune, and Amazon Q, making it a comprehensive platform for vector search solutions.
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.
Website: https://kdb.ai/
Category: Vector Database Engines
Tags: enterprise, scalable, vector-search, high-performance
KDB is a high-performance, scalable vector database designed for enterprise use, supporting billion-scale vector storage and retrieval. It is built to support production-grade AI applications with sub-100ms latency and high reliability.