⚡️The Rise and Fall of the Vector DB Category

**SPEAKER_1** (0:02)
Okay, hi, so this is another Lightning Pod with Joe Christian Bergam. Did I get it right? You're over in Norway?

**Joe Bergman** (0:09)
I'm over in Norway, Trondheim, Norway, in the center of Norway, yes.

**SPEAKER_1** (0:13)
What should people know about Trondheim?

**Joe Bergman** (0:15)
It's a small city. It's easy to get around. There's a great technical university here. The climate sucks a little bit, but it's easy to get things done in the winter. So yeah.

**SPEAKER_1** (0:26)
I've never been over. I've been to Oradev, I think, which is over near you guys. But yeah, what we're here to talk about just generally, your hot takes on rags, search, vector databases, all that stuff. I think you've taken to publishing a lot more recently on X, and that's gone really well. So I'll just go into that main thing that everybody knows you for, which is your piece on the vector databases, the rise and fall vector databases. So maybe you could give us the background of like why you felt compelled to write this.

**Joe Bergman** (0:59)
Yeah, first of all, I think I had to go a little bit back, right? So I have a long background in search and working on infrastructure for search. Like I've been in search, working on search systems for 20 years. At Yahoo Company, also fast search and transfer here in Trondheim and Norway. And also working on embeddings, neural search, all of those things, right? Leading up until ChatGPT, the ChatGPT moment, like November 2022 And then there was some kind of cookbook, I think from OpenAI where they said, okay, this is how you can do connect ChatGPT with your data and here's embeddings. And I think then a lot of developers, right, got into this is how we can build cert, this is how we can do RAG. I think there was like this unnatural connection, meaning that between retrieval in RAG, that it had to be a vector embeddings.

**SPEAKER_1** (1:57)
By the way, I have a small role in that. I actually was the one who wrote the Chroma example in the OpenAI cookbook. You did?

**Joe Bergman** (2:04)
Okay.

**SPEAKER_1** (2:06)
I was an angel investor in Chroma before they became a vector database, and then I was just helping out.

**Joe Bergman** (2:13)
I'm actually a huge fan of Jeff and Anton from Chroma. I mean, I think Anton left, but I think they've done a great job at promoting retrieval for AI and infrastructure, and they did a lot of great things. So I really enjoy talking to them on X. Anyway, and then we had the whole vector database. I think Pinecone was one of the pioneers framing it as a new infrastructure category. If you need to work on embeddings, you have to use a vector database. And naturally, then, if you want to do anything in AI, then you need to have a vector database. And that was my primary motivation for writing that piece and looking a little bit back, you know, what happened and where we are now and how I see it. And yeah, so that was the pure motivation.

**SPEAKER_1** (3:03)
Okay, and the general thesis, I guess, if you want to just sort of recap that, like, you know, I think it's a very fast rise and fall. Like Pinecone was a dominant player for a long, long time. And you know, I don't know my exact sources because there's a lot of rumors going back and forth. But apparently, they went up to like 100 million AR very, very quickly to raise a big round. And then suddenly, a lot of people started leaving. Like suddenly, it went from cool to uncool very quickly. And I don't understand why.

**Joe Bergman** (3:32)
I don't understand that either. And I think also they repositioned a little bit going back to their core messaging. If you go to their website now, it looks more developer focused. It's not the memory for AI. It's not like enterprise-ish. It's more towards developers now. So I think that they are trying to go back to their original roots. I think that's a good thing. But also, of course, there's been a lot of competition in the space. A lot of new companies. One of the upcoming stars is TurboPuffer, kind of same SaaS model, a little bit different pricing. And they really talk to developers. And I'm not saying that the companies are dying, right? I'm just saying that the separate infrastructure category is dying, right? Because you have Vector Search capabilities in almost any DB technology nowadays, right? And you have it also in more traditional search engines like Elasticsearch, Solar, Vespa. So I think there's like conversions on features on both parts. So and then you have things like PG Vector in Postgres. A lot of people, you know, get confused. Okay, I have already a DB. It has Vector Search. Why do I need another DB?