Vector Databases
- 01. Pinecone's vector database is fully-managed, developer-friendly, and easily scalable.
- 02. Weaviate is an open-source vector database. It allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly into billions of data objects.
- 03. Milvus - Store, index, and manage massive embedding vectors generated by deep neural networks and other machine learning (ML) models.
- 04. Astra DB - New vector search capabilities make this vector DB AI-ready by enabling complex, context-sensitive searches across diverse data formats for use in generative AI applications.
- 05. Atlas Vector Search is a fast and easy way to build semantic search and AI-powered applications.
- 06. Vespa supports vector search (ANN), lexical search, and search in structured data, all in the same query. Integrated machine-learned model inference allows you to apply AI to make sense of your data in real time.
- 07. Redis Vector Database - Build intelligent, AI-powered applications with Redis Enterprise.
- 08. Qdrant is a vector database & vector similarity search engine. It deploys as an API service providing search for the nearest high-dimensional vectors.
- 09. SingleStoreDB delivers built-in similarity search on vectors to add memory for your generative AI applications.
- 10. Relevance AI - Use managed or self-hosted vector databases to give LLMs the ability to work on YOUR data & context.
- 11. Chroma - The AI-native open-source embedding database. Chroma makes it easy to build LLM apps by making knowledge, facts, and skills pluggable for LLMs.
- 12. Deep Lake combines the power of both Data Lakes & Vector Databases to build, fine-tune, & deploy enterprise-grade LLM solutions, & iteratively improve them over time.
- 13. pgvector - Open-source vector similarity search for Postgres
- 14. Vald - A Highly Scalable Distributed Vector Search Engine
- 15. ScaNN (Scalable Nearest Neighbors) is a method for efficient vector similarity search at scale
- 16. Faiss is a library for efficient similarity search and clustering of dense vectors.