It seems to me like many developers are moving from native vector stores back to Postgres with pgvector. It's easier to integrate, cheaper to set up, familiar to work with, oftentimes faster, while not hitting any limitations for most projects. My vector store recommendation boils down to: "Use pgvector unless you have specific reason not to".
well it's apples and oranges. Why do people buy F150 instead of fitting things into the trunk of a Corolla? cuz they got a lot of stuff.
For people who run thousands of QPS on billions of vectors, Milvus is a solid choice. For someone playing with a twitter demo with a few thousand vectors, any vector db can do the job well. In fact there is a fun project Milvus Lite designed for that case :)
I've seen many builders migrate from pgvector to Milvus as their apps scale. But perhaps they wish they had considered scalability earlier.
People buy F150s because they find them cool and not because they actually need the space. Your Corolla could make deliveries around town in roughly the same time, while being cheaper and easier compared to introducing a new expensive car. In situations you need more space (which most of us won't), you can add a trailer instead.
pgVector is great and so is FAISS, but those are just a subset of what you get from Milvus. If all you need to do is RAG over 50Mb of documents then pick the right tool for the job. I use Chroma for a lot of projects.
Then, what if you want hybrid search, or different IVF variants, or disk-based search, or horizontal scaling, or something that leverages SIMD, or sparse vectors? Milvus is great.
The repo includes plpgsql_bm25rrf.sql : PL/pgSQL function for hybrid search ( plpgsql_bm25 + pgvector ) with Reciprocal Rank Fusion; and Jupyter notebook examples.
You start by underselling what can be done with Postgres and then follow up with the upper end of requirements that most projects won't need. My argument is exactly what you conveniently left out: The big bulk between the two.
It seems to me like many developers are moving from native vector stores back to Postgres with pgvector. It's easier to integrate, cheaper to set up, familiar to work with, oftentimes faster, while not hitting any limitations for most projects. My vector store recommendation boils down to: "Use pgvector unless you have specific reason not to".
Does this still hold?
well it's apples and oranges. Why do people buy F150 instead of fitting things into the trunk of a Corolla? cuz they got a lot of stuff.
For people who run thousands of QPS on billions of vectors, Milvus is a solid choice. For someone playing with a twitter demo with a few thousand vectors, any vector db can do the job well. In fact there is a fun project Milvus Lite designed for that case :)
I've seen many builders migrate from pgvector to Milvus as their apps scale. But perhaps they wish they had considered scalability earlier.
(I'm from Milvus so i could be biased.)
We regularly do tens of thousands of QPS on pgvector fine on massive data stores.
We dropped milvus after they started trying for force their zilliz garbage saas down our throats.
People buy F150s because they find them cool and not because they actually need the space. Your Corolla could make deliveries around town in roughly the same time, while being cheaper and easier compared to introducing a new expensive car. In situations you need more space (which most of us won't), you can add a trailer instead.
Interesting, I guess we're on the same page ;)
pgVector is great and so is FAISS, but those are just a subset of what you get from Milvus. If all you need to do is RAG over 50Mb of documents then pick the right tool for the job. I use Chroma for a lot of projects.
Then, what if you want hybrid search, or different IVF variants, or disk-based search, or horizontal scaling, or something that leverages SIMD, or sparse vectors? Milvus is great.
You can do hybrid search in Postgres.
Shameless plug: https://github.com/jankovicsandras/plpgsql_bm25 BM25 search implemented in PL/pgSQL ( Unlicense / Public domain )
The repo includes plpgsql_bm25rrf.sql : PL/pgSQL function for hybrid search ( plpgsql_bm25 + pgvector ) with Reciprocal Rank Fusion; and Jupyter notebook examples.
You start by underselling what can be done with Postgres and then follow up with the upper end of requirements that most projects won't need. My argument is exactly what you conveniently left out: The big bulk between the two.
How does Milvus compare to OpenSearch/ElasticSearch for hybrid search?
I think they are for different scales and scenario complexities.
> Milvus is an open-source vector database built for GenAI applications.
Milvus is the most popular vector database in the world.
Does it hold any value when it comes from the "Developer Advocate @ Milvus"
Like others have already said, pgvector is used a lot as well