> Filtering looks to be applied after coarse retrieval. That keeps the index unified and simple, but it struggles with complex conditions. In our tests, when we deleted 50% of data, TopK queries requesting 20 results returned only 15—classic signs of a post-filter pipeline.

Things like this are why I'd much prefer if Amazon provided detailed documentation of how their stuff works, rather than leaving it to the development community to poke around and derive those details independently.

Yes, I’m the founder and maintainer of the Milvus project, and also a big fan of many AWS projects, including S3, Lambda, and Aurora. Personally, I don’t consider S3Vector to be among the best products in the S3 ecosystem, though I was impressed by its excellent latency control. It’s not particularly fast, nor is it feature-rich, but it seems to embody S3’s design philosophy: being “good enough” for certain scenarios.

In contrast, the products I’ve built usually push for extreme scalability and high performance. Beyond Milvus, I’ve also been deeply involved in the development of HBase and Oracle products. I hope more people will dive into the underlying implementation of S3Vector—this kind of discussion could greatly benefit both the search and storage communities and accelerate their growth.

As far as I understand, Milvus is appropriate for very large scale, so will probably continue targeting enterprise.

I also didn’t see any latency info on their docs page https://docs.aws.amazon.com/AmazonS3/latest/API/API_S3Vector...

  At a glance, it looks like a lightweight vector database running on top of low-cost object storage—at a price point that is clearly attractive compared to many dedicated vector database solutions.

If it's this sensitive, there's a lot of companies staying on the sidelines until they can compute in person, or limiting what and how they use it.

Nitpick: AWS previously funded pgvector (the slow down in development indicates to me they have stopped). Their hosted database solutions supported the extension. That means RDS and Aurora were their first vector storage solutions.

I think of a search system doing quite a lot from sparse/lexical/hybrid search, metadata filtering, numerical ranking (recency/popularity/etc), geo, fuzzy, and whatever other indices at its core. These are building blocks for getting initial candidates.

Then you need to be able to combine all these into one result set for your users - usually with a query DSL where you can express a ranking function. Then there’s usually ancillary features that come up (highlighting, aggregations, etc).

So while S3 vectors is a fascinating primitive, I’m not sure I’d reach for it outside specific circumstances.

"That’s not linear growth—it’s a quantum leap"

"The performance and recall were fantastic—but the costs were brutal"

"it’s not a one-size-fits-all solution—it’s the right tool for the right job."

"S3 Vectors is excellent for cold, cheap, low-QPS scenarios—but it’s not the engine you want to power a recommendation system"

"S3 Vectors doesn’t spell the end of vector databases—it confirms something many of us have been seeing for a while"

"that’s proof positive that vector storage is a real necessity—not just “indexes wrapped in a database."

"the vector database market isn’t being disrupted—it’s maturing into a tiered ecosystem where different solutions serve different performance and cost needs"

"The golden age of vector databases isn’t over—it’s just beginning."

"The bigger point is that Milvus is evolving into a system that’s not only efficient and scalable, but AI-native at its core—purpose-built for how modern applications actually work."

AWS Athena hasn't received as much love for some reason. In the next two years I expect major updates and/or improvements. They should kill off Redshift.

We found our application was very sensitive to context aware chunking too. You don’t really get control of that in many tools.