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🔗 Picking a vector database: a comparison and guide for 2023
benchmark.vectorview.ai

In an era where semantic search and retrieval-augmented generation (RAG) are redefining our online interactions, the backbone supporting these advancements is often overlooked: vector databases. If you're diving into applications like large language models, RAG, or any platform leveraging semantic search, you're in the right place.

Picking a vector database can be hard. Scalability, latency, costs, and even compliance hinge on this choice. For those navigating this terrain, I've embarked on a journey to sieve through the noise and compare the leading vector databases of 2023. I've included the following vector databases in the comparision: Pinecone, Weviate, Milvus, Qdrant, Chroma, Elasticsearch and PGvector. The data behind the comparision comes from ANN Benchmarks, the docs and internal benchmarks of each vector database and from digging in open source github repos.

continue reading on benchmark.vectorview.ai

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