Modal Labs raised $355 million at a valuation of $4.65 billion. This was a funding round that my colleague Stephanie Palazolo first published details about earlier this month. Today we have Modal Lab CEO Erik Bernhardsson on the show to share with us more about his company's roadmap. Erik, welcome to TIV. It's great to have you here.
Thanks. It's great to be here.
Tell me in the simplest possible terms, can you explain to us what it is that Modal Labs does?
Oh boy. I always try to explain to my kids, but Modal basically helps companies run AI. So if you think about all these cool companies out there, building a lot of cool AI applications, whether that's Suno, which does AI-generated music, or Lovable, which powers a lot of live coding, they all need infrastructure. So we help them with the underlying infrastructure layer. And part of why that's needed is a lot of traditional infrastructure, if you go to the clouds, the hyperscalers, etc., they're just not built for AI. It's quite hard to work with these large models, iterate quickly, work with GPU capacity, whether that's, you know, training or inference or sandboxes or a bunch of other stuff. So we built this underlying layer that helps engineers move much faster and helps you kind of abstract away in a way you don't have to think about that underlying capacity routing, load balancing, all that stuff.
So when you say that the cloud providers, and I'm thinking about the hyperscalers, and then I'm also thinking about the neocloud companies, who we call them neoclouds, they call themselves AI-specialized clouds, but I mean their pitch, their whole pitch is that we make it easy for you to run your applications on our cloud. And so I hear you saying that they're not actually well suited then to run the application. So help me understand why you think it is that your layer needs to exist and that an application can't just go straight to a cloud.
Yeah, there's a couple of reasons. Like, the neoclouds are great, by the way. We use a lot of neoclouds. We work with a lot of hyperscalers. We consider them partners, a lot of them. What they give you is raw GPUs or raw hardware. So you have to still set it up, and typically the way you get access is you rent, you know, for a year or three years or something like that, like kind of fixed number of capacities. So you still have to work with like sort of fixed capacity. What a big problem is when you have a lot of these applications in production, your demand is actually very variable. You have this like, you know, peaks and troughs, and you know that depends on your users. So a big part of the challenge that a lot of people face when they deploy things into production running on GPUs is this variable demand. And so we help them basically get very flexible capacity. We have a fully usage-based model. You only pay for the time the GPUs are actually running. You can scale up to thousands of GPUs. You can scale down to zero. You only pay for that time. And we offer that flexibility because we pull together capacity from a lot of different neoclouds and hundreds of regions all over the world. The other thing is the developer experience. So when you get the hardware from these clouds, you still have to deploy the apps, set up maybe Kubernetes and Docker and manage all that stuff. So you need an infrastructure team. Whereas with Modal, we offer a much better developer experience. And with a few lines of code, you can just deploy something that auto-scales completely as demand comes in, scales up, scales down, you can hit it very quickly. You can have a research team that can deploy things to production themselves. You can build very complex applications.
Right, so you compete with the Fireworks, the Base10s, the Together AI, and now Modal Labs. I mean, this is what we call the infrastructure provider. That's sort of the segment of the market that you're competing in right now.
Roughly. Yeah, I would say all of them and Modal, what we have in common is we think of ourselves probably, or at least like we think of ourselves, I don't know if they think of themselves, but as a kind of a second cloud layer. So you have the underlying cloud with the capacity, and then we sort of manage a layer above, making that easy to access, making possible for engineers to quickly... We offer LLM inference as a service, but we also do a lot of other stuff. So I think we're a little bit more general purpose than many other players in the space.
So long term, I guess my question is why does the layer that you live in right now, this second cloud layer, why does it need to be an independent layer? In other words, long-term, I mean, this is a hypothesis I have. I mean, I could kind of see the cloud companies looking to you or to Together or to Fireworks. You know, even the neocloud companies, they're all building out their own software platforms as well. I mean, I sort of see the long-term game here being a company like you getting acquired by a cloud provider. Do you not see that to be the case in the long run?
I don't think that's true. I think I look at, you know, historically you look at companies like Snowflake versus Redshift. You know, Redshift was AWS's product. Snowflake was a standalone database. Snowflake is doing exceptionally well even though they're running on a lot of hyperscalers. And we think it's something similar here. We're building a service higher up. The neoclouds and the hyperscalers, they're very good, and we work with a lot of them. They're very good at offering capacity all over the world at a decent price, but it's quite hard to manage that capacity. But what they're very good at is putting that behind an API and making it possible to, you know, they're very good at running data centers, physical capacity. What they're often not that good at is, in my opinion, delivering good developer experience and delivering a software product that makes it possible to iterate quickly. And I frankly think of it as like a great partnership, like we're building that layer above and we're, you know, spending a lot of money on the underlying hyperscalers and neoclouds. And so in that sense, I think, you know, we don't necessarily think of it as a competitive dynamic. We think of it as a, you know, partnership.
What are you planning to do with the money that you raised?
Well, we're planning to grow very quickly. We are hiring a lot of people. We're quite acquisitive. So we might go out and buy some companies in order to move faster, as well as...
What do you think you... what are you looking to buy?
We like small teams that are extremely technical and maybe haven't had the commercial success they're expecting. There's a lot of companies out there that are exceptionally strong engineers building very cool things. And...
I mean, I'm thinking, you know, I don't know, like AI coding, for example, that's a place where developers have been flocking to. You know, maybe that's an area that you're trying to build more compatibility with. I mean, you know, maybe there's more on the back end integrating with the cloud company. Like, where do you think you could see yourself actually making an acquisition?
I think there's a lot of interesting stuff in complex AI stuff, you know, whether that's reinforcement learning or LLM inference optimization, things like that. Another bucket of things we're looking at would be things like file systems and runtimes and like very sort of low-level, you know, Linux and deep sort of, you know, management of infrastructure. A couple different buckets, but we're, you know, I think there's a lot of strong teams out there that we're very excited potentially working with.
