Business Breakdowns
· Matt Reustle, Alan Tu
[00:00:00] S0: On the point of connecting cloud to data, is it fair to assume that by transitioning to cloud, there would actually be more capacity for data to be stored or to be used? Like, is that connected in the sense that prior to cloud, the data capabilities might have been constrained? [00:00:00] S1: Yeah. One of the paradigm shifts of cloud was generally this concept of scale-out architecture, which basically allowed the ability to use more commodity hardware to be able to address larger and larger amounts of both compute and storage and data processing in this case. So, that was an important underlying trend that enabled this proliferation and this idea of just data explosion that I think you're touching on. When you look at what Apache Spark was, it was leveraging this concept of distributed compute and applying that to data processing. That was very important.
· Matt Reustle
[00:00:00] S0: The reason I asked is because it's always interesting to think about second-order impacts or a certain market enabling another market on the back of it, particularly with AI today, everyone looking for second-order impacts. So, it's interesting to hear how they're connected. And to your point, those three ideas, those three bets they made, certainly together compounded in many ways. On that point of the open source, there are all different ways to approach open source, and we have walled gardens versus open source, and there's the very famous Apple versus Windows example, or Apple versus Microsoft. Can you talk about the commercialization and that how that played into things? Because I know that was a major stepping stone for the business in terms of evolving from this tool that gained usership. Oftentimes, offering things for free is a great way to get people in; it has to be great, so it certainly was. But that feels like such a meaningful point in time for this business, and I'm curious how you would describe what happened and how they really turned this into a great product and evolved it into a great business.
· Alan Tu
[00:00:00] S1: There are a lot of examples of successful open source projects, and that's one of the magical things of open source is that it does enable a level of adoption. In the consumer world, we talk about capturing magic in a bottle. In the enterprise world and with technology, open source is a really good way of getting just massive amounts of awareness, mind share, and adoption. There are a lot of examples of successful open source technologies, but there are actually not very many examples of successful businesses that have been built on top of open source. And a lot has been made about, Red Hat was one of the first companies that built a business on top of Linux, but the reality is that it's actually one of the hardest things to do. You have to hit two home runs. This is actually one of the ways that Ali explains it: is you need to hit the first home run, which is to develop an open source technology that gets its mainstream adoption. But what a lot of folks don't think about is the second home run, which is, how do you actually build a business on top of it? And part of the problem is that the open source technology ends up becoming one of the business's main competitors because anyone can get the product for free, anyone, including your customers, but also including competitors. They can also leverage that technology, and actually, those competitors that have more distribution, more customer relationships, can actually do a better job of monetizing that open source technology. This is where again, that coming from academia, I think really helped inform the strategy for Databricks, which is the benefit of not being immersed in the commercial market is that you don't have the pre-existing notions of what you should or should not do when it comes to building a business on top of open source. Because again, the biggest example before that was Red Hat, which basically provided services and support for Linux, but that was the main thing. But because the Databricks team wasn't really aware of the precedence, they thought about things in a very first principles way. And when you think about this challenge of, how do you monetize something where there's a free alternative? It's actually a simple answer, and the answer is: you need to create a better product that is worth paying for.
· Matt Reustle, Alan Tu
[00:00:00] S0: Simple answer, maybe not simple execution. [00:00:00] S1: Totally. And part of the reason why it's not a simple execution is that if you've built your brand and gotten a lot of positive feedback for successfully creating the open source technology, it can be weird to then say, "I'm actually going to create a competing product, and I'm not going to put all of the bells and whistles into that open source technology." And for a lot of folks, that creates a lot of tension. You've got a lot of folks in the community that are like, "How could you do that?" There's almost a feeling of betrayal. When you're successful with open source, it can almost be a curse because you become very popular, you're viewed by technologists as someone who's brought this great thing into the world, and then all of a sudden, you need to be willing to be a villain. I think a lot of people have trouble making that jump. But I think again, in the case of Databricks, they just very much realized that there's no way we can compete unless we have differentiation. So, creating that differentiation was just a very important concept. And so, one of the things they did with Databricks, the company, and we can also talk about how they chose the name Databricks, was that they created a new implementation of Spark that was completely proprietary that had a lot more performance and things that enterprises want: reliability, scalability, etc. And they were just very unabashed about the fact that if you want to use this new implementation, you would have to pay for it.
· Matt Reustle
[00:00:00] S0: Is that comparable to the free tier of an LLM versus the pro tier of an LLM today? Just in terms of, you can feel the difference, and not everybody will have used the different tiers, but I'm trying to grasp the differentiation, to your point, needs to be there. It's very easy to describe it when you're signing up for a subscription to a website and it's like, "You get three free articles, but then after that, you have access to our proprietary database for the subscription cost." What comparison would you make, if there is an analogy, for the difference?
· Alan Tu
[00:00:00] S1: I actually think that analogy is pretty accurate, but there are nuances to that. As a consumer, we are used to this construct of there's a freemium version and then we pay for the premium version. And I think that decision about what goes into the premium version is where the nuance is. Again, going back to enterprise, the typical traditional wisdom is, "Oh, well the average developer that's working in their garage, we like that they can use this technology for free. We really want to monetize the enterprises, and so why don't we monetize the enterprise features?" Things like single sign-on and again, governance and security. That actually makes sense. But the reality is that while it is true that enterprises will pay for some of those additional features, how much will they pay for those additional features? And at the end of the day, those additional features are not the core product. So, going back to your example of the LLM, there are different ways of deciding how to paywall product. I think the analogy here would be to say it's, for certain freemium products, you can use it 10 times, but then the 11th time you have to pay. Or in other situations, there's extra features that you have to pay for, and then you have to go to the premium tier. The closest analogy in the case of Databricks would be actually the better model that is smarter and will give you better answers, you do have to pay for. And so, I actually think that comparison is not a bad one when you think about it through the lens of that core performance of the model, not just these ancillary things that you're paying for.