Pros and cons of vector search in elastic?

• ann-benchmarks

  51 4,588 8.1 Python

  Benchmarks of approximate nearest neighbor libraries in Python

- [Performance seems poor](https://ann-benchmarks.com/)

• qdrant

  140 17,839 9.9 Rust

  Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/

  

It depends on your requirements, for a simple hybrid-search solution, elastic, etc. should be enough. With growing data amount and if working not only with text embeddings, you should try out a dedicated solution, like Qdrant. https://github.com/qdrant/qdrant

• WorkOS

  workos.com sponsored

  The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

  

• Weaviate

  76 9,524 10.0 Go

  Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database​.

  

Highly opinionated as I'm working for Weaviate, so take my comment with a large portion of salt.

My highly opinionated view is that for Elastic, they're not really open source and the dependency on Java of the Lucene ecosystem is a big disadvantage, so as you already said, speed, they're getting better at this, but if you need to scale, this problem scales with you.

So if you already have ELK stack and don't need to scale, sure go for it otherwise, Weaviate offers real open source, so use it for free on your own infrastructure https://github.com/weaviate/weaviate

Suggest a related project