This feels similar to TurboBuffer, which is also built on top of S3 storage. TurboBuffer has been a leader in this space, powering vector search for major companies like Cursor, Linear, and Notion.

It seems AWS is leveraging its strong S3 brand to compete directly in the vector database market.

For more context, check out the TurboBuffer architecture docs and Notion’s presentation from the Data Council:

https://turbopuffer.com/docs/architecture

https://www.youtube.com/watch?v=_yb6Nw21QxA

(anti-disclaimer: I'm not affiliated with TurboBuffer in any way.)

I still contend that what most people want is traditional full text search, not another layer of black box weirdness behind the LLM.

You already have a model with incredibly powerful semantic understanding. Why do we need the document store to also be a smartass? The model can project multiple OR clauses into the search term based upon its interpretation of the context.

If you are using something like Lucene, queries are extremely fast and the maximum # of supported documents in one index far exceeds what AWS says they can support here.