You have to say that Pinecone got the timing absolutely right.
when were they founded?
Pinecone raises $100M Series B
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Re: Pinecone raises $100M Series B
#52What's the business model for companies like these? 750M valuation implies that they should be able to generate at least 75M revenue per year.
Public companies are trading far lower than 10x, they'll need to generate more revenue eventually.
Re: Pinecone raises $100M Series B
#53Perfect example of AI gold rush nonsense. Pinecone has zero moat and quite a few free alternatives (Faiss, Weviate, pg-vector). Their biggest selling point is that AI hype train people don’t Google “alternatives to pinecone” when cloning the newest trending repo (or I guess, ask ChatGPT).
> Pinecone has zero moat and quite a few free alternatives (Faiss, Weviate, pg-vector)
Faiss is a collection of algorithms for in-memory exact and approximate high-dimensional (e.g., > ~30 dimensional) dense vector k-nearest neighbor, it doesn't add or really consider persistence (beyond full index serialization to an in memory or on disk binary blob), fault tolerance, replication, domain-specific autotuning and the like. The "vector database" companies like Pinecone, Weviate, Zilliz and what not will add these other features to turn them into a complete service, they're not really the same. pgvector seems to be DB-backed IndexFlat and IndexIVFFlat (?) from the Faiss library at present but is of course not a complete service.
However which kind of approximate indexing you want to use very much depends upon the data you're indexing, and where in the tradeoff space between latency, throughput, encoding accuracy, NN recall and memory/disk consumption you want to be (these are the fundamental tradeoffs in the vector search domain), and whether you are performing batched queries or not. To access the full range of tradeoffs you'd need to use all of the options which are available in Faiss or similar low-level libraries which may be difficult to use or require knowledge of underlying algorithms.
(I'm the author of the GPU half of Faiss)
Re: Pinecone raises $100M Series B
#54I was using pinecone before installing pgvector in Postgres. Pinecone works and all but having the vectors in Postgres resulted in an explosion of use for us. Full relational queries with where clauses and order by etc AND vector embeddings is wicked.
Why do you use pgvector instead of pgANN? My understanding is pgANN is built with FAISS. When I compared pgvector with FAISS, pgvector was 3-5x slower. https://github.com/netrasys/pgANN
If I understand correctly, pgAnn is using a standard "cube" extension: https://github.com/netrasys/pgANN#setup
Re: Pinecone raises $100M Series B
#55They're so hot right now that you can't even signup for a starter account. I'm guessing this money will help them fix that so as to not slow down their potential customer base. It's a really easy DB to use for people with no idea about vector DBs, etc.
It "just works".
Re: Pinecone raises $100M Series B
#56Why not just use pgvector for Postgres. It’s free and works great. You can even do cosine distance between embeddings.
How fast is it compared to Pinecone?
Re: Pinecone raises $100M Series B
#57I was using pinecone before installing pgvector in Postgres. Pinecone works and all but having the vectors in Postgres resulted in an explosion of use for us. Full relational queries with where clauses and order by etc AND vector embeddings is wicked.
This hits home, it is a big ask to keep data in sync for yet another store. We already balance MS SQL and Algolia and all the plumbing required to catch updates, deletes, etc. adding another feels like a bridge too far. Hopefully MS will get on this train at some point and catch up to postgres.
Re: Pinecone raises $100M Series B
#58I think the real money in this space is going to be in custom applications of this tech, and it won’t need to be on Pinecone.
What do you mean by "this space"? Pinecone is ML infrastructure, search.
Re: Pinecone raises $100M Series B
#59Vector databases, so hot right now. I haven't really looked into them, I'm just wondering when or if postgres will do it better?
It already does and it’s free - https://github.com/pgvector/pgvector Only a sucker being forced to by their investors would use pinecone.
How big is your pgvector installation, and what are your p50/p99 latencies like? pinecone and the like are not necessary for toy projects.
Re: Pinecone raises $100M Series B
#60I’ve wanted to ask this question but I don’t know who to ask. Can someone explain what the use case is for vector DBs like pinecone, milvus etc. vs a fully featured search engine like Vespa, ElasticSearch etc. which also support vector search features? Is there something about running this type of index operationally that is particularly difficult?
They are for user-facing queries, where performance counts.