We’ve been running a bit of an Agent Cloud series surveying all the top inference/compute/cloud providers, from Databricks to Daytona to Railway and, even further back, E2B, but we’re excited to conclude this series returning to Modal, which has just raised a monster $355M Series C. The cloud was built for developers. But agents are now changing that. The old infra stack was designed for a human who could read docs, reason through YAML, and understand dashboards to figure out what they need when something broke. While this was painful for developers, it worked since they could fill in missing context in their heads. However, agents don’t have that luxury. Now in this new era of agents, everything has to be tighter. They need a place to write code, run it, inspect the output, change the environment, debug failures, and try again. Fast iteration and feedback loops with all the necessary context are crucial for agents to operate properly. Furthermore, sandboxes are a clear representation of this shift as agents can easily spin up isolated environments. This programmatic infra even extends to research: Two years ago, we were one of the first to cover Modal with CEO Erik Bernhardsson and Alessio designed our favorite LS thumbnail of all time: At the time, Modal was just a teeny little company with a $17M Series A. Today, fresh off their $355M Series C, Modal is one of the clearest examples of the agent cloud future being built in real time: a cloud platform moving past traditional web app assumptions toward the workloads AI actually creates such as elastic inference, sandboxes, GPU burst, post-training, background agents, and infrastructure that agents themselves can operate. In this episode, Modal CTO Akshat Bubna joins swyx and Vibhu to unpack why AI applications don’t fit traditional cloud assumptions, why Kubernetes was never designed for bursty compute-heavy workloads, and why Modal is now shifting from developer experience to agent experience. We go deep on Modal’s AI infra stack: serverless functions, decorator-based infrastructure, elastic inference for custom models, GPU snapshotting, DeFlash, speculative decoding, Auto Endpoints, sandboxes, persistent storage, networked containers, private IPv6, RDMA, multi-node training, and Modal’s capacity pool across 17 cloud providers. Akshat also explains why RL rollouts can require 100,000 sandboxes, why production agents need hard guardrails, why observability may matter more than reading code, and why AI has made infrastructure exciting again. We discuss: * Why Kubernetes wasn’t built for bursty AI workloads * How Modal started as a better runtime before becoming an AI cloud * Why Modal added GPUs before ChatGPT * The shift from developer experience to agent experience * Why observability matters when agents are writing the code * Elastic inference for custom models across audio, video, robotics, and comp bio * GPU snapshotting, cold starts, and why inference workloads are so bursty * Why RL rollouts can require 100,000 sandboxes * DeFlash, speculative decoding, and frontier-level inference performance * Auto Endpoints and making optimized inference easier to deploy * What Modal adds beyond vLLM, SGLang, and raw GPU rental * Modal’s 17-cloud capacity pool and supercloud strategy * Networked sandboxes, sidecars, private IPv6, and RDMA * Serverless multi-node training for post-training and research workloads * Auto-research, model-guided sweeps, and agents launching GPU experiments * Compute strategy, capacity planning, and batch tiers * Why production agents need specialized sandboxes and hard guardrails * Modal’s take on managed agents, CI, Gitpod/Ona, Python, TypeScript, and Modal Bench Akshat Bubna * LinkedIn: https://www.linkedin.com/in/akshat-bubna-188885103 * X: https://x.com/akshat_b Modal * Website: https://modal.com Timestamps 00:00:00 Introduction 00:00:39 Modal’s origin and why Kubernetes wasn’t enough 00:04:32 Developer Experience → Agent Experience 00:06:21 Modal’s AI cloud primitives 00:09:14 Sandboxes, agent loops, and proto-Cognition 00:12:12 Elastic inference, GPU snapshotting, and 100,000 sandboxes 00:15:24 DeFlash, speculative decoding, and Auto Endpoints 00:19:59 Production-grade inference beyond raw GPUs 00:22:00 Background agents, Ramp Inspect, and the agent lifecycle 00:24:08 Modal’s 17-cloud supercloud strategy 00:26:40 Networked sandboxes, private IPv6, and RDMA 00:32:48 Multi-node training, post-training, and auto research 00:37:36 Compute strategy, capacity planning, and batch tiers 00:40:55 Open models, real-time AI, and production agent infra 00:43:06 Hard guardrails, managed agents, and specialized sandboxes 00:46:06 Why AI made infrastructure exciting again 00:48:30 Model APIs, differentiated products, and agentic video 00:51:50 CI, coding-agent infra, SDKs, and Modal Bench 00:57:28 Closing Thoughts Transcript Introduction: Modal, Series C, and the Art Party Swyx [00:00:00]: We’re here with Akshat, CTO of Modal, together with Vibhu. Congrats on your Series C. Akshat [00:00:10]: Thank you. Swyx [00:00:11]: Your party yesterday was amazing. Akshat [00:00:15]: Yeah. Swyx [00:00:15]: From all the photos and all the swag. Akshat [00:00:17]: We had a bunch of art installations, which was fun, seeing, like, our products on pedestals next to, like, Rodin. Swyx [00:00:25]: Very nice. Very nice. When you started, it was not the GPU inference company. Maybe it was in your mind. Take us back to the origin story. Modal’s Origin: A New Runtime Beyond Kubernetes Akshat [00:00:39]: I first met Eric, who’s the CEO, through an investor. Back then Eric was already thinking about building, a new runtime, and he got there thinking through why are workflow orchestration products so hard to use. It’s because you have to run them on Kubernetes. Kubernetes is hard to manage. It’s not built for burstiness and, custom images, Swyx [00:01:03]: Yeah Akshat [00:01:03]: It has a terrible developer experience. Swyx [00:01:05]: And I’ll, I’ll interject Akshat [00:01:06]: Yeah Swyx [00:01:07]: For listeners, who are new, we interviewed Eric two years ago, and there’s a bit more of the story there from Spotify and all those things. Swyx [00:01:14]: And I came across Eric through Data Council because he did that talk on the serverless container stack that you guys did, which was like, that was my first like, “Okay, I need to take Modal very seriously” moment. Akshat [00:01:26]: Yeah. Swyx [00:01:26]: But it was still very unclear, like, do I need all this for just my data pipelines? Akshat [00:01:33]: Yeah. initially what we were thinking about was if we build a better runtime, it’s a very useful primitive in itself. It’s There’s a lot of things that, get solved by serverless functions, like you can do, ETL stuff, you can do job queues, you can do all this, like, bursty processing, which it turns out every company had needs for. but then we also were thinking about this as like, this is a primitive that we can build a whole collection of products on, which are very verticalized. So perhaps data engineering would’ve been the first one, but we were thinking about inference. Back then it was more classical inference, like computer vision stuff and running XGBoosts and whatnot. But we added GPUs to the product a year before ChatGPT came out. From Serverless Containers to GPU Workloads Swyx [00:02:19]: Nice. Akshat [00:02:19]: We just didn’t think it would be that big of a deal. Swyx [00:02:22]: Yeah, just like add A100. Vibhu [00:02:23]: Was there any, like, early key problem that really sparked off why you built it? Akshat [00:02:28]: Yeah. Primarily it’s just, none of the tooling that was out there was built for, one, a really great developer experience, and also there’s a general trend of, a lot of the workloads that we were seeing were very. I wish there was a better word for it, but compute-heavy. Like, they need, one, like, need a lot more resources, so you need to burst up and down a lot, versus like Kubernetes designed for, like, slow scaling and, more for, like, web server use cases. And also there’s just a lot more specialization in, like, what kinds of environments these workloads run in. Like, we had sometimes they need accelerators, sometimes they need different kinds of images, and this is just like a consistent thing that we saw across a lot of companies. That would be the next step. Software-Defined Infrastructure and Decorator-Based DX Swyx [00:03:13]: Yeah. Yeah. Be nice. I don’t know how much this factored into the early story, but I wrote a post when I was at Temporal about infrastructure, software-defined infrastructure or something like that. Akshat [00:03:22]: Yeah, the self-provisioning Swyx [00:03:23]: Self-provisioning. Akshat [00:03:24]: Yeah. Swyx [00:03:24]: Yeah. I can’t even remember my own post. Swyx [00:03:26]: And then you put me on the landing page. Akshat [00:03:28]: Yeah. We really like, the term and so we stole it. Swyx [00:03:32]: Because you had the insight that everything can just be in decorators co-located with the code, right? Akshat [00:03:37]: Yeah. Swyx [00:03:37]: Was that a big part of the original Akshat [00:03:39]: Yes Swyx [00:03:39]: Story or it was just like a DX layer? Akshat [00:03:41]: That was, really important because we really didn’t want people to spend, so much time, writing YAML, and it seemed like you could really condense the surface area of what you’re doing, put it in code so you can operate on it just like you operate on other code, and like build stuff that’s more expressive and dynamic. and so yeah, that was always a very important part. Swyx [00:04:04]: Then the pushback is this is a DSL. Akshat [00:04:07]: Yeah. Swyx [00:04:07]: It’s you’re closed source. I am locked into Modal. Akshat [00:04:11]: Yeah. We never really got pushback for that because the nice thing about Modal is you can bring whatever code you have, and su
