Vector databases

Vector databases, such as Pinecone, Qdrant, Weaviate, Chroma, Faiss, Redis, Milvus, and ScaNN, use embeddings to create query vector databases. These databases allow for efficient semantic searches.

• Improving language models by retrieving from trillions of tokens

VectorHub: Evaluation of multiple Vector databases

"Vector Hub is a free and open-sourced learning hub for people interested in adding vector retrieval to their ML stack. On VectorHub you will find practical resources to help you" VDB comparisons

Example vector databases

Please read this for more information Vector Databases (primer by Pinecone.io)

• https://github.com/Helicone/helicone
• Website Github

VectorHub: Evaluation of multiple Vector databases

"Vector Hub is a free and open-sourced learning hub for people interested in adding vector retrieval to their ML stack. On VectorHub you will find practical resources to help you" VDB comparisons

Platform Solutions

These platforms provide specialized storage solutions for AI applications, including vector databases, embedding storage, and traditional databases optimized for AI workloads.

Platform	Description
Chroma	Open-source embedding database for AI applications
Qdrant	Vector database for AI-powered search and retrieval
Milvus	Open-source vector database for scalable similarity search
Pinecone	Vector database optimized for machine learning applications
Weaviate	Vector search engine and vector database
NEON	Serverless Postgres platform for AI applications
Supabase	Open-source Firebase alternative with vector storage capabilities