Amit Singh’s Post

Missing one 😉

We do not call databases that support full-text search indexes Keyword Search Engines, right? The same applies to Vector Databases. Not every solution is a Vector Database if it simply supports a vector search index. There are only a handful of native and dedicated solutions where the whole architecture is built around vector embeddings and vector similarity search is not a just sidecar feature.

Another one missing is CrateDB with the same technology whether on the Edge, On-Prem, Hybrid or the Cloud.

This is indeed helpful. Thanks for sharing Amit V. Singh

👏 Absolutely! Just as Albert Einstein once said, "The only source of knowledge is experience."📚 Applying this to AI, foundation models and vector databases indeed serve as that "experience", enhancing generative applications. Keep innovating! 💡🚀

Thanks for the excellent overview and including Weaviate, Amit! Exciting times

Proud to partner with Amazon Web Services (AWS) to help customers power their GenAI apps! ✨

You are cranking out a ton of partner content this week. Keep up the great work. I wanted to add a global VectorDB-as-a-Service is supported inside the Seaplane IO AI Platform along with hosting the top LLMs, object store, compute, SQL, etc. Think "MLOps/LLMOps in a Box" if you will but it includes the runtime to manage and operate your production deployment. The idea is to be higher level of abstraction than your typical PaaS and thus all that is needed to call the VectorDB Service is this: