That said, it's no wonder people just pay extra for the simplicity of a slightly smarter endpoint like OpenAI. Sure, over time the costs are insane and you lack any flexibility to create a truly targeted solution, but it feels like an all-in-one easy fix.
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the way I see it is if you have a small amount of data (<10,000 vectors) then it's all the same and you should stick with the technology you are most familiar with
once you get more than that, you may want to consider a vector database
the reason that vector databases exist is because vector search is a highly compute intensive task, in regular database settings, you almost never have to run compute, the database is primarily looking to do an exact match
however, because vector search is predicated on the idea of finding similar vectors, and because exact vector matches are unlikely, you find yourself in the situation of having to optimize that
if you're building on a sql/nosql database you find yourself having to manage indexing, computing distance metrics, and load balancing
pgvector manages much of that for you, but due to the structure of SQL, it doesn't manage it in a very efficient manner - because it wasn't built to, an extra system needs to be built on top
as many experienced software engineers will tell you, adding complexity doesn't necessarily make something better, and adds more points of failure
purpose built vector databases like the ones in the article (eg milvus, chroma, weaviate) are built with this compute challenge in mind, and this becomes useful as the amount of data you have expands
