Well you can always move to tiger cloud if you want it managed :)
And this is PostgreSQL licensed just like our pg_textsearch which has been adopted to Googles AlloyDB and microsofts HorizonDB already, so there is hope!
I'm currently running VectorChord and I love it - with a fast backing disk, it's cut my postgres cost in half. Excited to see competition in low-RAM vector index tools. RAMageddon seems to be creating incentives to find more ways to take advantage of how fast modern nvme drives are!
> TLDR: We are launching pg_vectorsearch, a new open-source PostgreSQL extension that makes vector search dramatically faster and more cost-efficient. Its new PRISM index uses partitioning and compression instead of HNSW's graph-based approach, delivering faster queries with a fraction of the memory and significantly faster index builds. At 100 million vectors, PRISM achieved 6 ms query latency on a machine with 4× less memory than HNSW, with index builds over 20× faster.
There are zero hits for 'blog' on the README and zero hits for the file name 'benchmark'. It's not a me problem; I put in reasonable effort. I am not going to scour the README one link at a time.
It’s there. Titled “Announcing pg_vectorsearch: Scalable Open-Source Vector Search in PostgreSQL”. In paragraph: “ The result: an index that is significantly faster”
Edit: post filtering only
I don't understand how people are efficiently implementing tenant/user/permission scopes with these indexes.
But how are people doing recency biasing with vector databases?
All of our approaches are either expensive or super hacky.
> TLDR: We are launching pg_vectorsearch, a new open-source PostgreSQL extension that makes vector search dramatically faster and more cost-efficient. Its new PRISM index uses partitioning and compression instead of HNSW's graph-based approach, delivering faster queries with a fraction of the memory and significantly faster index builds. At 100 million vectors, PRISM achieved 6 ms query latency on a machine with 4× less memory than HNSW, with index builds over 20× faster.
I sent the thread to Erik (the main contributor) though, so if you have feedback or questions, good chance that he'll see it.
Here's the link to the numbers for your convenience:
https://www.tigerdata.com/blog/announcing-pg_vectorsearch-sc...