Not too long ago, AWS dropped something new: S3 Vectors. It’s their first attempt at a vector storage solution, letting you store and query vector embeddings for semantic search right inside Amazon S3.
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.
amazon s3 vectors.png
Naturally, this sparked a lot of hot takes. I’ve seen folks on social media and in engineering circles say this could be the end of purpose-built vector databases—Milvus, Pinecone, Qdrant, and others included. Bold claim, right?
As the engineering architect of Milvus and someone who’s spent way too many late nights thinking about vector search, I have to admit that: S3 Vectors does bring something interesting to the table, especially around cost and integration within the AWS ecosystem. But instead of “killing” vector databases, I see it fitting into the ecosystem as a complementary piece. In fact, its real future probably lies in working with professional vector databases, not replacing them.
In this post, I’ll walk you through why I think that—looking at it from three angles: the tech itself, what it can and can’t do, and what it means for the market.
A Surprising Fact: Vector Storage Can Cost More Than LLM Calls
Vector search is powerful, but it comes with a serious catch: it’s expensive. The computational demands are often one to two orders of magnitude higher than what you’d see in a typical NoSQL database. That gap isn’t just theoretical—it shows up in real bills.
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.
The 2022 ChatGPT boom only made this more obvious. Suddenly, embeddings were everywhere, and vector data became the fastest-growing data type on the public cloud. Retrieval-Augmented Generation (RAG) was the driver—and with it came three challenges that reshaped what a vector database like Milvus had to do:
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