Business Breakdowns
· Matt Reustle, Sam Eden
[00:00:00] S0: The developers, I assume, are then paying Cloudflare some software costs to use that? [00:00:00] S1: That's right. So similar to other cloud models, I'd say from the hyperscalers, their developer products follow a usage-based pricing model. So the more you use, the more you pay. But similar to the early products, they have a very generous free tier because they really want to attract that long tail of developers and then bring that into the enterprise, which is what they're doing at the moment. So there's over 3 million developers building using these Cloudflare developer products. A lot of them would be building quite sophisticated functions just within their free tier. To give a sense of how generous this free tier is, I think with their Workers serverless functions, you can query that up to 100,000 times a day. Their storage is 10 gigabytes per month with zero egress fees, which is dramatically cheaper than a lot of the alternative developer products. [00:00:00] S0: Can you just give an example of what a developer might build with the tools, just to give a sense of what a tangible example might be?
· Matt Reustle, Sam Eden
[00:00:00] S0: Can you just give an example of what a developer might build with the tools, just to give a sense of what a tangible example might be? [00:00:00] S1: Cloudflare Workers are best for quick functions that need to be done close to the user. Maybe one quick example would be that if you're loading a website, you might have a quick Workers script that changes the local pricing or changes the local language based on where that web page is loaded. That's done at the edge. So it's faster than querying, say, a central database to generate the page from scratch. The way to conceptually think about these Workers is anytime you can take a task and put it into bite-sized functions, it's good for that just because they spin up so quickly and then turn themselves off. It is a slightly different way of thinking because you're deploying again to the single global network. If you deploy a function to AWS, you might put that on the US East servers, so it lives there. So if you call that function, you have to travel there. Whereas with Cloudflare, if say you're building an app in New Zealand, you deploy a function immediately in London, someone can query that website and that same function is served from that London server at the edge. Everything gets propagated around really quickly, which is just another benefit of this single global network that they've built.
· Matt Reustle
[00:00:00] S0: Yeah, I can speak to that example as someone who tends to find myself on UK or Japan websites. Just the currency switch is always beneficial. Saves me some time from doing the currency conversion. When you think about AI – this is a business that has clearly benefited and took advantage of what the cloud has provided in terms of opportunities, been very thoughtful about that, and evolved naturally into different pieces of an organization and offering more. So two different things riding a wave, but also being operationally thoughtful about how they're going to market. When you think about where they fit into the AI boom, and the potential to be whatever they might be in that world, how do you frame it? And then how does the management team talk about where Cloudflare fits into the AI ecosystem?
· Sam Eden
[00:00:00] S1: I would say they fit kind of all around the AI industry. And there's probably four ways I'll describe this. First way is just the adjacent tailwinds, which you mentioned – as people want to use AI, then they're thinking more about the data strategy. And often you want your AI agents, for example, to read a lot of data and you might want to do that quickly without paying a lot of egress fees. So there's tailwinds in general for Cloudflare's approach for doing that. So that's the first relationship to AI is just adjacent services. The next three are the different sides of AI that they serve. The first is just serving the AI companies themselves. The latest reporting was that 80% of the top AI-native companies, the top AI companies – were Cloudflare customers. So that just shows that this next generation and this next era of businesses still look to Cloudflare first. So if they're providing other services for these AI companies, it positions them well to provide AI services as well. That's one, is just serving the customers themselves. The second direct AI involvement with Cloudflare is inference themselves. They've started to offer inference at the edge through their Workers AI product. It's a slight evolution from their previous strategy, where they still have this single global network, but previously every component – like every piece of that hardware – could provide every service, which is really powerful because it just meant the return on investment on each of those pieces of hardware could be split across all of their products. But now with AI inference, the hardware starts to matter. They had to install GPUs across their servers. They have servers across 330 different cities across the world. This was done really quickly. So Cloudflare is a very long-term strategic company. When they're actually designing their motherboards for all of their chips, they left an empty slot open, because they didn't know what would it be for or what the use case would be, but they needed it just in case something came up. And it turns out AI inference was that something. So they could go to all of their boxes and simply just plug in a GPU, and then AI inference is available across the world at all of these locations. This is serving these LLM models.
· Sam Eden, Matt Reustle
You can quickly query an open-source model for text generation, image generation, voice, or enterprise customers can deploy their own custom LLMs as well. And again, that gets propagated across their single global network. The AI inference from Cloudflare can be done really quickly at the edge. And as I mentioned before, Cloudflare Workers are really quick at spinning up and spinning down. So unlike, say, a hyperscaler, you don't have to pre-book or pre-provision capacity. You only use for what you pay for. If an inference is small and you don't use it for a day, you're not going to pay anything. So it only charges you directly on the AI inference that you use. [00:00:00] S0: It feels like a key layer of infrastructure. So you could see them on all sides of the market. Thinking about how they transitioned, you mentioned they got to a point where their offering was on par and then eventually better than what incumbents and legacy providers were offering. Can you talk about what that looks like? Because I imagine they broke into the enterprise market over time – what that looked like, how they approach it, some of the nuance to what it looks like to have an enterprise contract.
· Sam Eden
[00:00:00] S1: This is an ongoing evolution, their go-to-market changes. Three things I'll call out. One is just the general enterprise sales motion. The partner motion, which is really important to understand. And then their Pool of Funds bundling strategy. So the first go-to-market evolution was the enterprise sales. This is just a typical transformation from product-led sales to enterprise sales. And the product capabilities were there, but it's still a new go-to-market muscle. And across all companies, this is never an easy transition. Particularly as you're moving into security as well. It's a new buyer for them. Instead of just an IT administrator that is looking over the website, you're now selling to the whole security office. It could be a multiple-month process. So they had to build this new go-to-market muscle out. So in 2023, they actually saw their rep productivity start to drop, and they had to let go a lot of their sales team. But in 2024, they brought in a new president of revenue, Mark Anderson. He's incredibly experienced. He was formerly president of sales for Palo Alto Networks, CEO of Alteryx. So he has this deep experience with enterprise sales. This transformation – they're hiring a lot more reps. They've switched from hiring majority mid-market reps to now hiring a majority of enterprise reps. And they're still keeping this product-led growth because it's so important to their story. But now they're really increasing their sales-led growth motion. There's still ongoing transformation, but quick quantitative proof that we're starting to see is that the growth of revenue from large customers has started to inflect. It was around 30%, and now it's starting to inflect up to 40% year over year. It's a really impressive transformation.