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Author of this article.

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.

Anyone interested in this space should look at https://turbopuffer.com - I think they were first to market with S3 backed vector storage, and a good memory cache in front of it.

This is a good article and seems well balanced despite being written by someone with a product that directly competes with Amazon S3. I particularly appreciated their attempt to reverse-engineer how S3 Vectors work, including this detail:

> 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.

"I recently spoke with the CTO of a popular AI note-taking app who told me something surprising: they spend twice as much on vector search as they do on OpenAI API calls. Think about that for a second. Running the retrieval layer costs them more than paying for the LLM itself. That flips the usual assumption on its head." Hmm well start sending full documents as part of context see it flip back :).

  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.

They also didn’t mention LanceDB, which fits this description but with an open source component: https://lancedb.github.io/lancedb/

"That gap isn’t just theoretical—it shows up in real bills."

"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."

Postgres has pgvector. Postgres is where all of my data already lives. It’s all open source and runs anywhere. What am I missing with the specialty vector stores?

The biggest killer of vector dbs is that normal DBs can easily store embeddings, and the vector DBs just don’t then offer enough of a differentiator to be a separate product.

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

> Not too long ago, AWS dropped something new: S3 Vectors. It’s their first attempt at a vector storage solution

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.

S3 vectors has a topK limit of 30, and if you add filters it may be less than that. So if you need something with higher topK you’ll need to 1) look elsewhere or 2) shard your dataset into N shards to get NxK results, which you query in parallel and merge afterwards.

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

Betteridge can answer No to two questions at once!

This could be game changing

what do you think?

Does this support hybrid search (dense + sparse embeddings)? Pure dense embeddings aren't that great for specific search, they only hit meaning reliably. Amazon's own embeddings also aren't SOTA.

I don’t think it’s either-or, this will probably become the default / go-to - if you aren’t storing your vectors in your db like Neon or Turso.

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

I’m not sure S3 vectors is a true vector database/search engine in the way something like Elasticsearch, Turbopuffer or Milvus is. It’s more a convenient building block for simple high scale retrieval.

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.

It would be great to have the vector database run on the edge / on-device for offline-first and be privacy-focused. https://objectbox.io/ does this but i would like to see AWS and others offer this as well.

The cloud is someone else's computer.

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.

If you like to die in a slow and expensive way - sure.

S3 has much bigger fish in its sight than the measely vector db space. If you see the subtle improvements in features of S3 in recent years, it is clear as day, at least to me, that they're going after the whale that is Databricks. And they're doing it the best way possible - slowly and silently eating away at their moat.

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.

Since when was everything no longer "announced" or "released", but "dropped"? Is this an LLMism?

By hosting the vectors themselves, AWS can meta-optimize its cloud based on content characteristics. It may seem like not a major optimization, but at AWS scale it is billions of dollars per year. It also makes it easier for AWS to comply with censorship requirements.