Business Breakdowns
· Matt Reustle
[00:00:00] S0: Yeah. And I'll reframe it maybe, and you can tell me how accurate this is versus inaccurate. Most of what we talk about with agentic is oftentimes just shrinking the context, giving it very deep context in specific tasks, and therefore the quality of the response is going to be much stronger. It's not going to be pulling random places on the web. Databricks obviously has the most rich data to use in terms of informing those models and therefore can partner with businesses who might want to develop those agents.
· Alan Tu
[00:00:00] S1: Yeah. So, to your point, part of what the industry is realizing is that there are techniques that are important to be leveraged to build effective agentic applications. For example, RAG, which is retrieval-augmented generation. There's a whole set of processes around enabling that, with vector databases, for example, embeddings. So, Databricks' offerings there. There's also a very important part of building agentic applications is around model evaluation. So, because of the very unpredictable nature of the large language models, it's not quite as easy to always know exactly, "Is our application or agent acting the way that we think it should?" And so, there's a whole set of technologies around model evaluation, around being able to actually quantify, " How are these models behaving? Are they doing what we think that they should be doing?" And Databricks again is building products around that. In the same way that it's parallel going back to the machine learning era where you would go through a similar process around evaluating models there. You can get a sense of the fact that while there's a lot of focus on the core large language models, if you actually want to build applications in production, there's so much around and beyond just the model that is really where I think Databricks has a strong right to win.
· Matt Reustle
[00:00:00] S0: Yes, the infamous question of where the value might accrue within the layers of the AI ecosystem is quite interesting. On the opposite side of the equation, there's been this interesting, solid, good relationship with the cloud infrastructure providers. AWS is there. Does that change at all with AI? Because to your earlier point, the data cleanup and all of that becomes even more important. I have personally been able to clean up unstructured data into structured data much more cleanly with AI. Different story when it comes to me versus doing this at cloud scale, but what's your view on where that relationship, where the cloud providers haven't necessarily moved into this category, does that change at all? Is that a risk? Is that something that you think about at all?
· Alan Tu
[00:00:00] S1: Well, it's funny you mentioned that cloud providers haven't really moved into Databricks as a market because they actually do have offerings there. And I think it's actually more reflection of the fact that Databricks has done such a good job of both executing on product but also just market positioning that you sort of have that, yeah, but I would say that for as long as I've followed Databricks, and I first met Ali over 10 years ago, they had just signed their first strategic partnership with Microsoft, that was actually Databricks branded under Azure, as Azure Databricks. And that was an incredibly important partnership to jumpstart Databricks's monetization. I bring that up because Databricks, from day one, Ali as a business leader has always been extremely pragmatic and strategic about how they operate vis-à-vis the hyperscalers. And so, what I would say is that what has always been the case is that there is "coopetition" with the hyperscalers. The reality is that for customers that are using Databricks, they will also be consuming infrastructure, compute, and storage of the hyperscalers. So, there is a benefit for the hyperscaler clouds when customers are using Databricks on top of their infrastructure. And that coopetition dynamic, I totally expect to continue in the world of AI. But I do think it's a very important question because this is a big enough market that the hyperscalers do care about it. And that it's been another, I believe, underappreciated strength of Databricks, which is their ability to align themselves with the hyperscalers. There are a lot of examples of companies that came out with a great product, had a tremendous amount of momentum, and then Microsoft decided that this was too strategic for them to lose, and so they were going to put all of their weight behind killing that product. I think we can all think of different examples here. And that is oftentimes been a real challenge for growth-stage software companies. I think Databricks has just done a very good job of never positioning themselves in such a way that the hyperscalers are 100% incented to kill them. There's enough alignment, there's enough opportunity for partnership and mutual growth, that that relationship with the hyperscalers has generally been relatively synergistic despite the fact that they do represent very real competition.
· Matt Reustle
[00:00:00] S0: I bring it up all the time, but Amazon with something like FedEx and UPS, where they were relying on them, and then FedEx and UPS couldn't deliver during the holidays, and it was a big problem. To the extent that Amazon built out a network and then eventually started to compete with it. But there is something to the coopetition being of high enough quality where it's not creating a problem. There are other dynamics that get involved, but that's very useful framing. I did want to get a little bit more into just some of the financial dynamics. You mentioned the $4 billion in ARR at this point. How does it work from a customer perspective? Is it a simple usage-based revenue model?
· Alan Tu
[00:00:00] S1: It is, yeah. So, the way to think about it is that Databricks charges based on the actual compute that's being utilized for any of the workloads that are on Databricks. And so, you can again going back to a tangible example, your credit card fraud example, every single time the customer wants to run an analysis and everything that happens underneath that in terms of the pipelines that pull in the data, that incurs compute cost. And that's how Databricks aligns itself from a monetization perspective. I would say that, we've talked about this open source piece, but Databricks has been really smart beyond just the core usage-based pricing about recognizing when certain features or products are strategic but when they actually have a right to charge for those products. One example that Ali is given is that when you think about your smartphone, you have one of the features of the smartphone is your address book. The reality is, the address book is an incredibly important feature, not just for phone calling, but a lot expands from having an address book in your smartphone. But the reality is that no handset maker is going to be able to charge for the address book. And so, Databricks has a lot of different products that are analogous to the address book where they are effectively, again in many cases, using open source to give it away for free in order to get adoption, but that are actually still very, very strategic. So, one example of that is one of their big value propositions is providing a governance layer on top of all the data so that enterprises have a single pane of glass for which they can see all the metadata that they're processing. And so, I guess all this is a long way to say that while Databricks does use a usage-based pricing based on compute, the reality is that at least from my perspective, they're actually monetizing more than just compute. It's a way of monetizing a lot more layers of value that Databricks is providing, and there's an intertwined nature to all the different things interacting with one another which represents value, even if it's not directly correlated to the compute cost.