Picking a vector database: a comparison and guide for 2023

Amber Ivanna Trujillo
4 min readApr 18, 2024

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.

A comparison of leading vector databases

Navigating the terrain of vector databases in 2023 reveals a diverse array of options each catering to different needs. The comparison table paints a clear picture, but here’s a succinct summary to aid your decision:

  1. Open-Source and hosted cloud: If you lean towards open-source solutions, Weviate, Milvus, and Chroma emerge as top…

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Amber Ivanna Trujillo

I am Executive Data Science Manager. Interested in Deep Learning, LLM, Startup, AI-Influencer, Technical stuff, Interviews and much more!!!