Especially seeing these days you can run a vector store on-disk if you have less than 10 million records, pull any free embedding model straight from HuggingFace and run on consumer hardware (your laptop).
In principle you could totally run this on a single bare-metal node, but most will not be doing that in practice.
why is storing the file as a FAISS/LanceDB on-disk vector store not "cloud native"? I am running this setup in production across dozens of nodes, we migrated all of our infrastructure off Pinecone towards this solution and have seen 10x drop in latency, and the cost improvements have been dramatic (from paid, to totally free).
I have a bit of an axe to grind in the vector DB space, it feels like the industry has gaslit developers over the last year or so into thinking SAAS is necessary for vector retrieval, when low latency on-disk KNN across vectors is a solved problem.
Also I'm experimenting in further integrating things to reduce latency and most likely will publish another article within the month. Stay tuned.
Finally I somewhat agree that many of the players in the vector DB space try to push their cloud offerings. Which is fine, how else should they make money? And if latency matters that much to you, Qdrant offers custom deployments, too. I believe running Qdrant locally will handily beat your LanceDB solution perf-wise unless you're talking about less than 100k entries. We have both docker containers and release binaries for all major OSes, why not give it a try?
All that is to say, maybe there's a lot of money in the SAAS/big cloud space, and customers willing to run their own setup that requires tuning might not be willing to hand them large sums of money? Just theorizing here!
Oh also "cloud native" is like a marketing term vaguely saying "you can hook this into other cloud stuff" and it works with K8s/whatever cloud thingy.
Edit: another thought, skip lambda entirely and run the embedding job on the server as a background process, and use an on-disk vector store (lancedb)