Warm welcome to Basil Chatha, our newest cohost from the Forward Deployed side of the AI Engineering world! We’ll be ramping up a new show with him and couldn’t pass up an interview with Modal’s CEO Eric on the occasion of their monster Series D!
Two years ago, we spoke with Erik Bernhardsson about his vision for truly serverless AI infrastructure: developers should be able to run code without provisioning clusters, predicting load, or paying for idle resources. CTO Akshat then joined us to talk about Modal’s infra primitives and stack to offer those primitives and agent experience.
A lot has changed in the past 5 months. Since their $5B series C, Modal now reports it crossed $1 billion in annualized run rate, raised a $750 million Series D at a $16B valuation, and has spun up nearly three billion sandboxes. In this conversation, Erik joins Vibhu to explain the infrastructure and economics behind that growth—and why demand for compute rarely looks like the contracts used to buy it.
Watch the full conversation on YouTube →
GPU demand isn’t rectangular
A typical capacity contract gives a customer a fixed number of GPUs for a fixed period. Plot capacity against time and you get a rectangle. Real workloads behave differently: they grow, spike, and fall throughout the day. Buy enough for the peak and you pay for idle hardware; buy less and you risk missing demand.
Erik describes Modal’s bet as a kind of duration transformation: pool demand across thousands of customers, secure capacity underneath, and let each customer use what they need when they need it. The smoother the aggregate demand, the more useful that pool can be. It’s a useful companion to our conversation with SF Compute’s Evan Conrad: Evan focuses on making capacity tradeable; Erik focuses on making it usable in smaller, elastic slices.
There is a financial risk on the other side. If a provider locks in capacity at today’s prices and GPU prices later fall, it can be left holding expensive supply. Erik says he doesn’t expect an imminent collapse, but thinks the possibility matters when building the business. Jump to the GPU price discussion →
The sandbox that almost didn’t make it
Modal launched sandboxes after Erik and cofounder Akshat Bubna noticed that language models could generate code—and that someone would need a place to run it. Early adoption was modest enough that the team discussed shutting the product down. Then coding agents, background agents, and reinforcement learning created new demand. Erik says sandboxes have grown to roughly a third of Modal’s revenue, a result he didn’t predict at launch.
That growth also changes what infrastructure has to do. An agent may create an environment, run code, inspect the result, and try again many times. The latency of starting and restoring those environments becomes part of the experience. In our recent interview with Akshat, we explored this shift from developer experience to agent experience; here Erik adds the product and business story. Jump to the sandbox discussion →
Fast startup is an infrastructure feature
Elasticity only works if capacity can arrive quickly enough to matter. Modal’s serverless GPU write-up describes the machinery: a buffer of available GPUs, a custom filesystem, process checkpointing, and GPU memory snapshots. Those pieces reduce the work needed to bring another model replica online. It is a concrete example of how infrastructure decisions show up in an application’s responsiveness and cost.
We also discuss Erik’s path from Spotify’s recommendation system to Modal, how the company buys compute, alternatives to NVIDIA, forward-deployed engineers, and why code review is optional at Modal. Jump to engineering culture →
Show notes
Modal — the AI infrastructure platform Erik founded.
Erik’s writing — his essays on infrastructure, markets, and building companies.
Modal’s Series D announcement — the company’s latest reported growth and sandbox milestones.
How Modal achieved truly serverless GPUs — the startup and scaling techniques behind elastic inference.
Modal’s resource solver — how it selects capacity across changing prices and availability.
Scaling to one million concurrent sandboxes — an engineering account of sandbox scaling.
Our first interview with Erik — the original serverless infrastructure thesis.
Our interview with Akshat Bubna — infrastructure and developer experience for agents.
The Programmable Data Center — Evan Conrad on GPU trading and physical settlement.
Timestamps
0:00 What Modal does
1:25 From Spotify to building Modal
4:19 Founding before the generative AI boom
7:43 First customers and fundraising
10:00 Building for inference and autoscaling
13:21 Where Modal gets GPU capacity
15:39 Renting vs. owning infrastructure
19:45 The risk of GPU prices falling
24:18 Is AI demand infinite?
26:12 Alternatives to NVIDIA
28:25 What Modal can learn from neoclouds
30:26 GPUs as an asset class
31:55 Modal’s products: functions and sandboxes
36:03 The case for smaller training workloads
41:21 How agent sandboxes are changing
46:48 Anthropic and sandbox security
49:29 Using agents to test security
52:51 Developer experience in the age of agents
57:45 What Modal’s forward-deployed engineers do
59:55 Modal’s approach to product management
1:03:29 Optional code reviews and engineering culture
1:05:56 Cloud, on-prem and bring-your-own-cloud
1:09:25 Capacity, fundraising and growth
1:13:07 Scaling the team
1:17:28 Erik’s tweets: ephemeral sandboxes
1:18:31 The problem with rectangular GPU contracts
1:21:20 Inference providers vs. model builders
1:23:25 Where models improve next
Transcript
Automatically transcribed from the revised edit; names and wording may contain errors.
0:00 Thanks for coming on, Eric Fern-Hartson from Modal. Could you explain the business a little bit and maybe how you got here? Yeah. So we build, the easiest way to put it is we build AI infrastructure. So we offer a compute platform and a storage platform that enables a lot of customers like Dacagon, and Suno, and Runway, and Cognition, and many others to essentially build AI applications. So what that means in practice is a lot of inference, a lot of sandboxes, sometimes training and fine-tuning, sometimes batch jobs and other things. So we have basically built a software SDK that enables customers to move really fast and build this. We manage all the capacity underneath, make it easier to deploy and scale and run these things in production. So you were on Light and Space like two years ago, a lot has happened since then. How big were you back then, like whatever numbers you can share and comparatively, how big are you now? I actually don’t remember where we were, but it’s probably like at least 50X, there’s revenue and users and users. Yeah. I use it actually for a side project that I have. Nice. Yeah. It’s like, yeah, turn 2D images into 3D models. Yeah. So yeah, just hosting a couple of models here. What do you think? Any feedback? I mean, I love it. Nice. Good. I mean, yeah, that’s why I use it over any competitors or anything. Good. So before you started Modal, you were at Spotify and also like better.com, I think.
1:30 That’s right. Yeah. Yeah. What were you doing at Spotify? I actually did a lot of different things at Spotify, but my main contribution was I built a music recommendation system. The first version I joined after school in 2008, 2009 maybe, and spent seven years at Spotify. And most of that time was building basically from scratch and music recommendation system and built up a small team and we launched a bunch of features like related artists and Discover Weekly and a bunch of other stuff. So a lot of, a lot of matrix factorization, a lot of compute intensive stuff. And arguably like a lot of what sort of later became the genesis of Modal was just building AI applications and realizing like the infrastructure fucking sucks. Like, there’s got to be a better way to do that. So a lot of sort of, you know, the rationale for Modal was like kind of building a tool I always wanted to have throughout my years at Spotify. Yeah. So Modal was more of an offshoot of the stuff you learn at Spotify, not much from Better.com. Better.com was a little bit of a side tour. It was kind of a roller coaster, a long story about the company. We talked about it separately. But I was a CTO for five years of better and grew to a tech team from, I think, you know, two to three, four hundred. So I learned a lot about management and building teams and hiring and also learned a lot about developer productivity. I thought a lot about tools and things like that. Did a lot more like full stack stuff. But yeah, I kind of came back to my, was close to my heart, which is infrastructure and AI and machine learning after that. What was Better.com doing again? Mortgages. Okay. Yeah. How did you get into that?
3:00 I mean, we don’t have to spend too much time on that. Yeah, no, it’s fine. Like this guy, Zach Frankel, who I didn’t know at that time, ended up recruiting me to Better as a CTO and the company was 10 people. And, you know, for whatever reason, you know, the founder of Vishal, very interesting guy, very, very smart, a little crazy in mostly positive ways. We got very well along. So we, yeah, we started building it in 2015 and I spent five years growing that company and then, you know, eventually turned out that doing mortgages is pretty hard and, you know, interest rates went up very quickly after the pandemic and that cost a lot of issues at the company. But by then I had already left. That was like 2020, 2021 when you left? Yeah, something like that. Yeah. So, OK, you left before you had the idea for a model or you left and then you started? It’s hard to say. I had a lot of random ideas when I left Better and I, I actually wasn’t sure if I wanted to start a company. I was kind of just, you know, during the pandemic, I sort of almost took like a sabbatical at two kids. I mean, I still have kids, but I mean, at that time there were there were I think two and four and so I decided to hang out and took some time off and was kind of working like part-time, like hacking on certain things and wasn’t sure maybe I should do some consulting and I started building stuff. And I just got like super pumped about this idea that eventually became model. But actually both, you know, model was like, I think the 30 I built that I got, you know, that so there were like a few attempts before then that went nowhere.
4:30 But my model was like the first idea was like, this is kind of cool. Like I should do this. This is like, this could be a really cool platform. Were the other ideas like also in a similar vein like developer tools? The first thing was like some sort of web scraping thing, which went nowhere. But actually, you know, in hindsight, maybe could have gone somewhere. And was this before the chat GBT launch? Yeah. Yeah. 2021. Oh, OK. So chat GBT was late 2022, right? Then I started building, so maybe actually to rewind a little bit context, like when I worked at Spotify, I released an open source framework, Kaluigi, which is like workflow orchestration. So sort of the same categories, airflow and pre-effect index or sort of dated categories to some extent, like people don’t talk about as much. But for a while, it was like, you know, got a lot of attention. Anyway, I never turned it into business. But a couple of years later, when in 2021, when I was thinking about business ideas or things to work on, I kind of came back to workflow orchestration. And I started hacking on a bunch of ideas of, you know, running code in the, you know, like basic orchestrating code running in the cloud. And what I realized was like, you know, at the end of the day, like core puzzle pieces, not just like orchestrating the computer, it’s like executing the computer. And I was like, that’s kind of a bigger problem actually to solve. So in a way, I actually like, you know, left it on the shelf and then said, like, actually, I’m going to like go to this other thing over here and I’ll go back. I’ll get back to the workflow scheduling a little bit. But, but like, you know, I never got back to long story short and ended up building modal, which is like the focus is more like, let’s just figure out a way to make it easy to run code in the computer, in the cloud. And the focus was initially like fairly general purpose.
6:00 And it actually in a way like it’s always, we’ve kind of gone full circle more recently, but, but we focused on so many different applications. It’s kind of the idea was like, let’s just make it super easy to run code in the, in the cloud. Could be GPU code, AI, machine learning. It could also be like data stuff. And focused a lot on, you know, how do we scale it up? How do we make the developer experience awesome? Realized super early that like in order to do that, we’d have to go very deep in a stack and built on file system, built on container runtime and did a lot of, you know, solved a lot of hard engineering challenges. And then you mentioned chat, CPT, that was actually not quite the killer app. But like, if you remember, like that fall, another thing came out around the same time, which was stable diffusion and stable diffusion turned out to be a killer app for our, for what we’re building at that time. We were only four or five people at that time. So we started seeing a lot of people coming to us and wanting to run a stable diffusion model. And then we were real, that, that, that made us realize like maybe gen AI inference is actually a really good use case for us. And how are you finding it? I’m just like compulsive tweeter. I was even more like now, like I’m too distracted by building a company, but I always like tweeted a lot. I was blogged a lot. So I think I had a little bit of a following. And so, you know, in the early days, I like a lot of what drove the growth was just like me just compulsively just like tweeting about model and like what I was building and getting people excited and writing long blog posts about where the cloud is going and all these things. And I call it a bit like founder led marketing. I always like feel like you kind of have to lean into like what you’re good at
7:30 and like what I’m good at is like, I guess like just shit posting and being terminally online. And so, you know, like work, it’s still like, you know, I think a lot of our growth is we’re just kind of good at social media and just like being, you know, online a lot. Nice. How long until you had your first customer? It took a while. Also, did you raise like early on? We didn’t raise for a while. I actually self funded it for a while, even with up until the first few employees, which in a way was like kind of nice because it may be really like frugal about money and like wanting to see like ROI. I wasn’t taking out a salary for a while for a long time. Actually, at some point it became a problem because I needed the money, but we didn’t raise for a while. And until early 2022, we raised the seed rounds. So at that point I’d already been working on it for about a year. Akshah joins the CTO and co-founder in about five years ago, about now August, maybe 2021. But then it took until like basically like late 2022 for us to see any meaningful revenue. I guess that’s why I’m proud of you. Yeah. And stable diffusion then in our case was the main thing that’s sort of, so we went from like, you know, $100 a month in like, I think like November, 2022. And the next month we did like $2,000 a month. And then like January, 2023, we did like 30,000 all of a sudden. And, you know, so finally I felt this like massive, just like anxiety thing off my chest. I’m like, maybe this is actually like we’re onto something. It feels like we’re onto something. And then 2023 we grew very fast.
9:00 And I guess we’ve been growing very fast since then basically. That 30 K a month moment, was that just like a couple of big customers or was it like very diverse? It’s just one. And I don’t know if I ever talked about like who that was and if they even know it, but I think they suspect it. I