Why AI Infrastructure must evolve for Agent Experience — Akshat Bubna, Modal CTO

Modal CTO Akshat Bubna argues that AI infrastructure must evolve from developer experience to agent experience, as agents require tighter feedback loops, sandboxes, and programmatic infra. Modal, which raised a $355M Series C, is building an agent cloud with elastic inference, GPU snapshotting, and 17-cloud capacity pool to support bursty AI workloads like RL rollouts requiring 100,000 sandboxes.

We’ve been running a bit of an Agent Cloud series surveying all the top inference/compute/cloud providers, from Databricks https://www.latent.space/p/databricks to Daytona https://www.latent.space/p/daytona to Railway https://www.latent.space/p/railway and, even further back, E2B https://www.latent.space/p/e2b?utm source=publication-search , but we’re excited to conclude this series returning to Modal, which has just raised a monster $355M Series C https://modal.com/blog/modal-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 https://tracxn.com/d/companies/modal/ tHK2ShUcB0Q1o6j-hbJ-xcZMxDsw0P3kCJ85veVeYjU . 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 https://modal.com/products/inference , sandboxes https://modal.com/products/sandboxes , GPU burst, post-training, background agents, and infrastructure that agents themselves can operate https://modal.com/solutions/coding-agents . 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 burstyWhy RL rollouts can require 100,000 sandboxes DeFlash , speculative decoding, and frontier-level inference performance Auto Endpoints and making optimized inference easier to deployWhat Modal adds beyond vLLM , SGLang , and raw GPU rentalModal’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 tiersWhy production agents need specialized sandboxes and hard guardrails Modal’s take on managed agents , CI , Gitpod/Ona, Python, TypeScript, and Modal Bench Akshat Bubna Modal Website: https://modal.com 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 sure, the DSL is at the configuration layer for, what hardware you’re using, how you’re scaling things up, but you still own the code. Akshat 00:04:27 : And that’s, that’s been an important, part of our story, even as we do inference now. Swyx 00:04:32 : Yeah. Vibhu 00:04:32 : How much of do you think still stays the same today? Like if you were to build something today, DevX very important, but I feel like, a lot of this has been changed with just hook it up to an agent, have Claude Code, have Codex implement a tool. there’s very agent native primitives that are different than if I’m doing this myself, right? Developer Experience → Agent Experience Akshat 00:04:54 : We’ve changed our SDK team to think about agent experience instead of, developer experience and we think that the same benefits that apply for DX also apply for AX, which is why would you have an agent read through hundreds of Kubernetes files and like write YAML that’s not even typed when it can make a couple of changes in a decorator and it gets this self-provisioning runtime of, being able to see its changes live in action? yeah, it just seems from the customers we talk to, they find Modal is much faster for agents to use versus operating on a different substrate. Swyx 00:05:34 : Yeah, because like you, again, you co-locate the infrastructure requirements to the code that runs it. Akshat 00:05:38 : Yeah. Swyx 00:05:38 : Well, the negative thesis now is that nobody’s looking at their code anymore, so there’s no point. Akshat 00:05:44 : Yeah, people aren’t looking at code. one thing we still see is really important is observability. Swyx 00:05:51 : Yeah. Akshat 00:05:51 : Like how good is your dashboard? And of course, like we have, we push a lot of it to the CLI so the agents can do their own investigation, but you still need humans to go interpret what’s going on and, make judgment calls and whatnot. and that’s I feel like, Maybe more important now than looking at the code itself. Swyx 00:06:11 : Yes, because like, you can try to treat the code as a black box and then use, see the observable action that comes out of it, and then just prompt a change. What Modal Is For: AI Cloud Primitives Akshat 00:06:21 : Yeah. Swyx 00:06:22 : So I think it takes a bit of restraint to not specialize, to say, “I want to ship a new primitive,” and then just be general purpose. Swyx 00:06:31 : People ask you, “What are you for?” You’re like, “ I don’t know. We can do this, we can do that.” Vibhu 00:06:36 : Well, I’d be curious to see, like, okay, if we were to ask you, like, what is Modal for even at a high level? There’s a lot you guys do, sandboxes, GPUs, everything. How do you answer? Akshat 00:06:46 : Modal is a cloud platform that’s built for, where we’ve built the primitives from scratch for AI applications. and right now it covers, inference, training, batch processing, and sandbox workloads. Akshat 00:07:00 : But we’re building a lot more Swyx 00:07:02 : I noticed you didn’t say web server, so there is still a role for, like, the always-on large-scale Kubernetes type things. Akshat 00:07:09 : Yeah, absolutely. We’re, we’re not trying to compete with the renders of the world, because yeah, we think the differentiator for us is the, are the workloads that need specialized compute, need to scale up and down a lot. yeah, they’re, they’re, they’re just shaped differently. Working Alongside Frontier Startups Vibhu 00:07:26 : I think you’re building a lot of it alongside the startups, right? They’re innovating quite a bit, even in your, like, latest blog post. Like, even in the series C, the customers that you mention here, the cognitions, technical ones, ramps and whatnot, they’re, they’re innovating with you, right? And that’s not something AWS is doing directly with. Akshat 00:07:45 : Yeah, absolutely. I think, this is again classic. We’re a small team. We can move really fast. our engineers are working with our customers and figuring it out. Yeah. Swyx 00:07:54 : So my first week at Cognition, I walked in, there was someone wearing a Modal shirt. I was like, “What are you doing here?” They’re like, “Yeah, I just. I am embedded inside of Cog.” Akshat 00:08:05 : Yeah, I think that was Peyton. We sent him over Swyx 00:08:07 : Yeah. Akshat 00:08:07 : Because, the latency of communication was too high otherwise. Swyx 00:08:12 : Yeah, distributed node, you have to - you have to place one and collocate. Vibhu 00:08:16 : Yeah. Swyx 00:08:16 : So I had a, I had direct personal experience, right? So I worked on smol developer three years ago. it was inspired by Claude 1. I think you onboarded me at some point, like, just before, and I was like, “Oh, like, I need some bursty compute. Like, I was just gonna try using Modal.” And it was a, it was a pretty pleasant experience. apparently, I showed up in the board meeting, like the analytics. smol developer, Sandboxes, and Proto-Cognition Akshat 00:08:39 : Yeah, you blew up on Hacker News and, Swyx 00:08:41 : Yeah Akshat 00:08:41 : We got a big traffic spike. I. I think the way you used smol developer was Modal functions for running stuff, which was. Like, the, that was a good use case. but then, yeah. Swyx 00:08:53 : Yeah. That - So to me, that was proto-cognition. Akshat 00:08:55 : Right. Swyx 00:08:56 : If only I had, like, stuck to it. Swyx 00:08:58 : Like, that was like, if - did you say draw the tech tree Akshat 00:09:00 : Absolutely Swyx 00:09:00 : You’re just like, “Yeah, like, probably this will happen.” Akshat 00:09:02 : Yeah. Like, he was so close. You were just rebuilding upon us Swyx 00:09:04 : I just didn’t realize. Akshat 00:09:05 : But the funny story there is at the same time, we were talking to a bunch of customers who needed something like sandboxing. Swyx 00:09:14 : Yeah. Akshat 00:09:14 : This is like twenty-three. Swyx 00:09:15 : Yeah. Akshat 00:09:16 : So we built Swyx 00:09:17 : You introduced a new API right after that. Akshat 00:09:18 : Yeah. Swyx 00:09:19 : Yes. Akshat 00:09:19 : Like, we built sandboxes in May of twenty-three before anyone was even knew this was gonna be a thing. And the first example we published was, we took smol developer Swyx 00:09:28 : Smol developer Akshat 00:09:28 : And put it in a loop, so the agent can iterate on itself. Swyx 00:09:33 : Loops are hot these days. Vibhu 00:09:34 : It’s the looper. Akshat 00:09:34 : Yeah. Vibhu 00:09:35 : Loops in. When was this, twenty-three? Akshat 00:09:38 : Yeah. Vibhu 00:09:39 : A small check. Akshat 00:09:39 : Yeah. Swyx 00:09:39 : It’s like twenty-three. so the. the, those for listeners, like, the problem was the models are not built for any of this, right? Swyx 00:09:46 : Like, you’re just trying to like. They’re not post-training to understand, like, looping and, like, self-correction and tool calling was there, but, like, also not that great. Akshat 00:09:55 : Yeah. Akshat 00:09:55 : I don’t remember if you used tool calling in this one, but yeah, the models would just diverge after like ten iterations and not produce anything meaningful. Swyx 00:10:03 : Yeah. But like, then. So okay, like now talking to myself three years ago, the answer Vibhu 00:10:08 : Of course they will get better Swyx 00:10:09 : Collect all the failures, build benchmark, and then collect all the, examples, build the RL environment Akshat 00:10:15 : Right Swyx 00:10:15 : Sell it for like ten billion dollars to Meta. Swyx 00:10:17 : And then also train a model and then sell that for sixty billion dollars to Elon. And this is Akshat 00:10:23 : Yeah, of course Swyx 00:10:23 : The funny machine. Like, it’s like, it’s about the hardware. Akshat 00:10:28 : It’s hard to have that inherent conviction that the stuff will get that much better. Swyx 00:10:33 : In retrospect, it’s so fucking obvious. Akshat 00:10:36 : Fair enough. Swyx 00:10:37 : Like, what else were we doing back then? I don’t know. anyway. Yeah. So this. That was the start of your sandboxing journey, right? I feel like it didn’t blow up until, like, last year. Akshat 00:10:49 : Yeah. Swyx 00:10:50 : So there was like a couple years of quietness. Akshat 00:10:52 : Exactly, yeah. We were Vibhu 00:10:53 : I think very underrated product value. Like, my experience with Modal, Charles, before he had joined Modal, met this guy at a hackathon, and he really insisted we wanted to run some small model, not hosted anywhere, and he’s like, “ there’s this cool company, Modal. They’ll like spin up a GPU sandbox, we can throw it on there. They’ll take a Hugging Face link.” And like there’s so much value just right there, right? Like instant hosting, spin it up, spin it down. It’ll stay cold, but we run the demo a few days later, it’ll come back up and like all this stuff in retrospect, like it’s still what we needed like today. Akshat 00:11:27 : Yeah, it’s still needed today. workload shapes have changed a lot as, we run stuff for people with really massive production scale and, there it’s it’s not about scaling from zero to one, but it’s how do we scale really elastically, from like thousand to fifteen hundred GPUs very quickly in a given region. It’s the same shape problem. Elastic Inference, GPU Autoscaling, and Custom Models Vibhu 00:11:50 : Okay. So you look at, say, Cursor Composer, right? Akshat 00:11:53 : Yeah. Vibhu 00:11:53 : They had a. “We’ll do RL on a model every couple hours.” you guys have a whole version of RL inference gym and whatnot. Vibhu 00:12:01 : When you look at workloads like that, you’re doing train runs where you need to scale up, scale down every hour thousands of GPUs, right? That’s the example for we do need it, right? Akshat 00:12:12 : Yeah. Well, so I’ll, I’ll take a step back and, maybe talk about like how people use Modal today. because our biggest use case is, elastic inference. And the thing we first found product market fit, with was inference for custom models. So we stayed away from the LLM space, and we were serving companies like Suno for audio, Runway for video, robotics, comp bio companies that train their own model elsewhere. But Modal is the best black box that for deployment, scaling to however many GPUs you need as your traffic pattern changes. And we saw all of them like have a very unpredict- predict- predictable, traffic pattern. it’s like diurnal. It’s Some days, like the company will do a launch and, they’ll need like, way more. And it’s not just one model that they deploy. They-- all these companies deploy, lots of different models in different regions, and so the autoscaling problem becomes even harder because then you have to scale within a certain region, and those cycles are offset. So different times you scale up in different regions. Akshat 00:13:20 : So that’s like our sort Vibhu 00:13:22 : And that Akshat 00:13:22 : Yeah Vibhu 00:13:22 : That in and of itself is a huge category. There’s a bunch of inference providers which, provide this fireworks, does this as a service together, whatnot, Base10. that’s carved into its own niche for language models, at least right now. Akshat 00:13:36 : Yeah. the thing that we have specialized in is the autoscaling aspect. Vibhu 00:13:41 : Yeah. Akshat 00:13:41 : Because we found that it’s not universally true that everyone else can autoscale, and we’ve gone deeper into it on the tech side by, we’ve incorporated GPU snapshotting into the product so we can take the GPU state, like your torch.compile model, snapshot it, and the next cold start is way faster. And so going back to your question, it’s That’s why you need a lot of burstiness for inference. But then people also do a lot of demand training, like for RL stuff, your rollouts are bursty, as you said. People also do a lot of batch jobs. So we’ll see, a lot of companies, before they have a training run, they’ll need thousands of GPUs to run encoding or something like that. And I think those things are much more bursty than. I agree that agents are not that bursty. sandboxes are, except when you’re doing RL. RL is just RL, Batch Jobs, and 100,000 Sandboxes Vibhu 00:14:28 : Or commerce Akshat 00:14:28 : Insanely bursty. Vibhu 00:14:29 : Yeah. Akshat 00:14:30 : Yeah. Like when you’re doing, rollouts, you sometimes need a hundred thousand sandboxes in your sandboxes. Vibhu 00:14:37 : Yeah. I’m curious if you’ve seen early sparks of continual learning. There are some people, like our friends, ngram, recently announced this Akshat 00:14:45 : Yeah Vibhu 00:14:45 : They’re, they’re trying to do training. That also seems like a different workload, right? If you’re doing training twenty-four/seven per se, there’s a very weird dynamic of how you’re using GPUs between people and whatnot, but seems like something you guys would work for. Akshat 00:15:00 : As you said, we’re, we’re fortunate to work with a number of, customers at the frontier and grab some of our customers. and they are taking the primitives we have, and trying to use them in very interesting ways, like continual learning. It’s possible as the stuff gets better, some of that will be part of, our offering as well if, more people need it. but we’re, we’re just waiting to see Vibhu 00:15:23 : Yeah Akshat 00:15:23 : How it shakes out. Vibhu 00:15:24 : Is there a primitive that you added after sandboxing that was the next step in the story? LLM Inference, DeFlash, and Speculative Decoding Akshat 00:15:32 : I guess we’ve been going much deeper into LLM inference Vibhu 00:15:35 : Yeah Akshat 00:15:35 : Because we realized that some of the advantages we have with like autoscaling, again, especially in different regions and whatnot, are, not present elsewhere. and the place where we had a gap was we weren’t, working on the model layer itself. Like we were a black box. And, we realized that, we can get to frontier-level model performance, with, by having great people who work on this. And, we’ve been open sourcing a lot of our work, in terms of, Recently, we, shared our work on DeFlash, which is a block-based, speculator, and we’ve open sourced, all of it. So, you can - By using open source DeFlash, you can get the same performance as you would with one of the proprietary providers. And the next thing we’re thinking about here Vibhu 00:16:23 : I thought this was Akshat 00:16:24 : Yeah Vibhu 00:16:24 : An interesting blog post as well, right? Like, I think in here you make a claim that. Not a claim, just that how effective speculative deco-decoding really just get to. Akshat 00:16:33 : Yeah. Vibhu 00:16:33 : Anything you wanna point out from this around, what people should know? Akshat 00:16:39 : Yeah, absolutely. the high-level summary is, it would help to describe what speculative decoding is. Vibhu 00:16:44 : Yes. Akshat 00:16:44 : I will, yes. Vibhu 00:16:45 : I think, like Akshat 00:16:46 : Yeah Vibhu 00:16:46 : So we’ve covered like Eagle and all this Akshat 00:16:47 : Yeah Vibhu 00:16:47 : Like Hydra and all those things, but it was like two years ago. Akshat 00:16:51 : Yeah. Vibhu 00:16:51 : I think it doesn’t hurt, right? Akshat 00:16:52 : Yeah. Speculative decoding is you have a smaller model, called a draft model, predict tokens ahead of the bigger model, and then you have the bigger model, verify all of this, all the tokens are predicted. And the reason it’s faster is if you’re predicting, one token at once, you’re bound by memory bandwidth. But if you can batch the verification of, the draft model, then you’re much more efficient using compute, and it’s faster, and as long as your draft model is producing a lot of tokens that can get accepted, which is called the accept length, you can get a speed up that’s, multiple times of, the original model speed. and well, that’s what we highlight here. It’s Like people talk a lot about we made these kernels faster and whatnot, but improving kernel will only give you like few percentage points of improvement, and, increasing accept length, literally is a multiplicative decrease Vibhu 00:17:47 : Like two to four X. Akshat 00:17:48 : Yeah, exactly. Vibhu 00:17:48 : Without much head-on performance. Akshat 00:17:50 : Yeah. I think it may - you are running a second model, right? So it may be something more expensive in the compute, Vibhu 00:17:57 : I meant quality performance Akshat 00:17:58 : Probably not by much Vibhu 00:17:58 : But yeah. I think Akshat 00:17:59 : So there’s no drop in quality performance Vibhu 00:18:01 : Yeah Akshat 00:18:01 : Because you’re always. You’re never accepting a token that the big model Vibhu 00:18:04 : It’s strictly better Akshat 00:18:05 : Yeah Vibhu 00:18:05 : Or it’s same. Akshat 00:18:06 : Exactly. Vibhu 00:18:07 : Right. Yeah. Akshat 00:18:08 : And so we’ve been working a bunch on DeFlash, which is a block-based speculator. so it’s instead of predicting, one token at a time, it’s predicting a block. And we’ve been open sourcing our work with it. The next thing for us here is for helping people train speculators and custom models. it’s it’s something that traditionally is very forward-deployed engineering driven, support deployed, engineer driven, like you work with customers and help them do that. And our vision for. This is why we launched Auto Endpoints, is we want to make frontier-level performance available to everyone. And so, we mentioned this in the announcement, we teased it. The next thing we’re, we’re launching is, as you run an auto endpoint, we shadow traffic Auto Endpoints and Frontier-Level Performance Vibhu 00:18:54 : Do you want to explain what auto endpoints are? Akshat 00:18:57 : Yeah. Vibhu 00:18:57 : I lovely, yeah. Akshat 00:18:58 : Yeah. So, this is, I guess, going back to your Modal is you touch the code, but, sometimes people don’t wanna touch the code, and they wanna get started with an endpoint that works and has all the great performance and, scalability that Modal has. So we’ve made that easier with, a way to create an endpoint from our UI, from the CLI, that has all of our optimizations that we talked about, like the DeFlash stuff already baked in, and there’s full transparency. So we give you the code, you can go run it yourself, and if you want, you can eject out into the full Modal experience, which we see as people get sophisticated, they do wanna tweak the models, they wanna, fine-tune stuff. You can still do all of that. It’s it’s not a black box. And yeah, the next thing, as we teased later in the post, is how do we give you value even beyond this in terms of having your draft models evolve as your data distribution evolves, again, without having to talk to a person and, yeah. Vibhu 00:19:59 : I guess just to understand it directly, you have the GPUs, you have an endpoint that’s compatible, you serve open model. If someone was to do this themselves, what’s the delta that you guys provide? So you do a lot of open source great work on effective inference. how does it compare to, say, I take the same model, 5.2 FP8, take shelf inference engine, vLLM, SGLang, get compute of similar capacity, similar cost. What’s the delta that plugging into something this, like this offers outside of the benefit of, scaling? Production Inference Beyond Raw GPUs Akshat 00:20:34 : It’s interesting because we’ve taken the approach of open sourcing our contributions and upstreaming them. we work closely with the SGLang team. We want the improvements that our team, comes up with to be, there in open source for others to use, even outside of Modal. The benefit to us is we have a team that has significant expertise in terms of if you do have something that is not there, our team can help you get that performance, first. the other thing is with these endpoints, we are way more elastic, as you said, than, anyone else, and you have true scaling to zero. you have true, burstiness, and in practice, that matters a lot more to people than just finding, the GPU and, running Modal code on something. Vibhu 00:21:20 : Yeah. And I will say it’s not that straightforward to just. like what I said is easier said than done, right? Akshat 00:21:26 : Yeah. Vibhu 00:21:27 : It’s I think still for the average person, still hard to just gut check using different. There’s, there’s quite a bit of combinations you can make there. the trade-offs aren’t really known at face value. Akshat 00:21:40 : Yeah. it’s it’s not just that. I think it’s it’s that running production-grade inference is a hard infer problem. Vibhu 00:21:49 : Yeah Akshat 00:21:49 : Even if you subtract out the autoscaling Vibhu 00:21:50 : Yeah Akshat 00:21:51 : Is controlling things like tail latency and, making sure every, request is delivered at least once and whatnot. The Model and Agent Lifecycle Vibhu 00:22:00 : There’s a lot of innovation that you can do here. I think, it’s very interesting that you’re starting to encroach on, like as you become a full cloud, you’re starting to encroach on other people’s turf. Vibhu 00:22:09 : What will you not do? Akshat 00:22:13 : Well, we wanna follow our users and, make sure they get like a platform that has everything that works well together. so right now we’re focused on the model lifecycle and the agent, lifecycle. so both like going from data prep to training to inference, and then also if I want to deploy a background agent, let’s say, sandbox, do persistent storage, a whole bunch of other stuff. Vibhu 00:22:38 : We talked to Cole, who did, OpenInspect. Yeah. Akshat 00:22:42 : Yeah. Vibhu 00:22:42 : And RealInspect also is on Modal. Akshat 00:22:44 : Yeah. So Ramp Inspect was a great example of a background agent that was really successful because they, were able to use some of the primitives like snapshotting and fast scaling to just have something that feels really reactive and works well. Ramp Inspect and Background Agents Vibhu 00:23:02 : Yeah. That’s the new CTO of, Ramp right there. Akshat 00:23:05 : Yeah, Rahul. Vibhu 00:23:08 : It was really fun. yeah, okay, I think, all very bullish. Like, one of my reflections was also I did not originally. So when I met you guys The Inference Inflection: CPU, GPU, and Co-Location Vibhu 00:23:19 : You weren’t that much in the GPU game, and now you’re all about, inference. And one of the points that I hinged on for Jensen’s keynote at GTC this year was, what we’re calling like the inference inflection, right? That let’s say in AI workloads or machine learning workloads, it used to be like, let’s call it eight to one GPU to CPU, and now it’s more like one to one, which is like a interesting. Like, - because of how much agents are blocked or call out to this, to CPU heavy stuff the actual, like, limiting factor, like, swings back and forth from GPU to CPU a lot more than it used to be all GPU and then occasional CPU. Akshat 00:24:01 : Yeah. Vibhu 00:24:02 : GPU, CPU. And now it’s like just constantly, and you just have to locate everything. Seventeen Clouds and the Supercloud Strategy Akshat 00:24:08 : Yeah. And that’s one of the things that, again, we see as, something appealing about Modal, which is we’ve built this capacity pool that spans, 17 cloud providers, so we’re, we’re very good at Running on various kinds of cloud capacity across the world Swyx 00:24:24 : You don’t have your own data centers? Akshat 00:24:25 : We don’t have our own data centers. We just run across a lot of neo clouds Swyx 00:24:29 : Yeah. Are Akshat 00:24:30 : Metal providers. Swyx 00:24:30 : Yeah. Question mark. Swyx 00:24:31 : Yeah. You’re, you’re running the math, and you’re like, “What’s the cutover point where you’re like.” Akshat 00:24:36 : Yeah, it’s a good question. part of it is we see our differentiator in the software layer, and, being capital light and focusing on the software helps us move really fast. so far it’s worked out well because there are so many other people building data centers that we’re able to work effectively with them, and again, focus on what makes us, special. Swyx 00:24:55 : Yeah. Swyx 00:24:56 : 17 gets you into, like, the local providers sometimes. Like Akshat 00:25:00 : The, Swyx 00:25:01 : Which was the most interesting one? Akshat 00:25:02 : There are a lot more neo clouds than you expect, and they all have various degrees of, various levels of reliability. And, that’s why it’s something we’ve invested a lot of time in, is building our own reliability layer on top. so if the GPU falls off the bus or something happens, we user workloads are not affected, and that lets us use a lot more capacity than, Swyx 00:25:30 : Yeah Akshat 00:25:30 : You as a user would be able to. Swyx 00:25:32 : It’s a useful thing to have because like now everyone knows, like, what layer you are and, like, you optimize for being the super cloud of all clouds. Akshat 00:25:41 : Yeah. That’s, that’s, that’s the idea. and so I guess when you mentioned colocation, that’s, that’s another interesting thing where, one thing we’ve seen is people come to us when they want, very specifically located, CPUs or GPUs, like they want Swyx 00:25:57 : Oh, they pin it in like Akshat 00:25:58 : Yeah Swyx 00:25:58 : EU? Akshat 00:25:59 : Exactly. Or EU, US. Swyx 00:26:01 : Right. Data resiliency Akshat 00:26:02 : Australia Swyx 00:26:02 : Locality thing or performance or what? Akshat 00:26:04 : It’s either data locality or latency, yeah. Swyx 00:26:07 : Yeah. Akshat 00:26:07 : Like, you want your. They’re running sandboxes and model. They want them to be right next to a Swyx 00:26:10 : Yeah, it’s easy then Akshat 00:26:11 : Yeah Swyx 00:26:12 : To. That is important in all those things. and so, like, you’ve accidentally, I don’t know if it’s accident, but, like, you’ve built the perfect primitive for agents to express themselves. And then, like, it’s almost very funny how every extra development just involves more file system, just involves more CPU. Akshat 00:26:30 : Yeah. Swyx 00:26:31 : Just like the things that you already have. I don’t know much about, if there’s any, like, networking usages that are interesting, but you’ve also done some good work on networking. Networking, Sidecars, Private IPv6, and Sandboxes Akshat 00:26:40 : Yeah, that’s exactly right. Like, we’re just taking compute storage and networking and building stuff on that layer, for, again, the stuff people need. Swyx 00:26:49 : Yeah Akshat 00:26:50 : We see a few interesting networking things coming up. one is people want networked sandboxes. so we have Swyx 00:26:57 : For like a Docker cluster type thing. Akshat 00:26:59 : Yeah. Swyx 00:26:59 : Sorry, Docker Swarm. Oh, fuck. What is it called? Akshat 00:27:02 : Compose. Swyx 00:27:03 : Compose type thing. Akshat 00:27:04 : Yeah. So if you want Docker Compose, our sandboxes now support, this thing called sidecars. So you can. A sandbox is a pod of containers, and you can run multiple containers in, a sandbox. also useful because, going back to networking, people want a lot of control over, outbound networking from a sandbox. Swyx 00:27:23 : Yeah. Akshat 00:27:23 : Like, they might wanna run a middle proxy for, like, maybe logging stuff for RL or, controlling how egress can happen to a domain, injecting credentials. and yeah. So we’ve, we’ve had to build a lot of that stuff ourselves. Swyx 00:27:38 : Yeah. Akshat 00:27:39 : But then also sometimes people want, sandboxes spanning multiple nodes to talk to each other, which is an emerging thing we’re seeing. We have support for that for a different reason, and yeah, we’ll see if that becomes stable. Swyx 00:27:52 : Like, just an open socket. It’s a. This is directly like mTLS. Akshat 00:27:56 : We do support that, which is you can, expose a tunnel inside a sandbox. Swyx 00:28:01 : Yeah. Akshat 00:28:01 : And then you can either expose it to public internet or it can be, you can add like a HTTP, auth layer above it. But we have this thing called I6PN, which we haven’t talked about, which is this, like, overlay network using IPv6 addresses. so if Modal containers, within the same workspace, when this is enabled, can address each other using this private IPv6 address, and no one else can. Akshat 00:28:28 : So it’s like private networking, for containers. We built it because we needed it as a primitive for our distributed training product. so we have this other feature, which is you can add a decorator to a function, and you get a cluster of GPUs. and they have RDMA networking. so you can run a distributed training job, that’s truly serverless. and we did the overlay network for that. But then we’ve seen that people are using it for other reasons, and, I’m intrigued to yeah, what would people do with it. Swyx 00:28:59 : Build primitives and let people figure it out, right? Akshat 00:29:01 : Yeah, exactly. Swyx 00:29:02 : You put out a pretty interesting Akshat 00:29:03 : They’re like, they read the docs webpage. Let me use that Swyx 00:29:06 : Yeah Akshat 00:29:06 : Something they never intended to work. This is literally not even in our docs page. People somehow found it, and they’re using it. RDMA, Memory Movement, and Distributed Training Swyx 00:29:12 : Huh. Swyx 00:29:14 : The way you portrayed it with, like, RDMA versus TCP, like, very well laid out, but just the transfer speed change at scale for RL, like yeah, you have it, you have it built in. I’m sure someone found it. It’s found it to be a lot more efficient before you made a thing out of it, right? Akshat 00:29:32 : Yeah. And not to split hairs, I guess the overlay network is the TCP overlay network. Akshat 00:29:39 : The reason we have that is you need that to do the key exchange for RDMA before you set up the RDMA network on top of that. but then people found the TCP part. Swyx 00:29:48 : Can I tell you, this is like a big aha moment for me because Akshat 00:29:51 : Yeah Swyx 00:29:51 : So I review 2,200 submissions for the World’s Fair. Akshat 00:29:56 : Yeah. Swyx 00:29:57 : And then I got this from John Osterhout Akshat 00:29:58 : Huh Swyx 00:29:59 : Who I don’t know if. Do John Osterhout by name? Akshat 00:30:01 : The name sounds familiar. Swyx 00:30:02 : He published a. He’s a well-known professor, published a lot of interesting software design books, and this is the talk he chose to submit, is on RDMA at Inference. And I’m like, you wouldn’t think that this guy, who is like operating systems guy, would care about RDMA. Akshat 00:30:20 : I, it makes sense to me because I, Swyx 00:30:24 : This is the cloud, right? Yeah Akshat 00:30:25 : Like, the way you move around your KV cache and how efficiently you can do it, how efficiently you move, your weights from your training GPUs to your inference GPUs in RL is there’s a lot of degrees of freedom, and it is a systems problem Swyx 00:30:41 : Yeah Akshat 00:30:41 : Moving memory around Swyx 00:30:42 : Yeah Akshat 00:30:43 : Scheduling. Swyx 00:30:44 : This shows you how primitive my understanding of networking stuff is. Swyx 00:30:46 : Is this like the domain of WireGuard as well? Akshat 00:30:50 : Not quite. Swyx 00:30:51 : It’s adjacent? Swyx 00:30:53 : Explain everything. Akshat 00:30:54 : Sure. Swyx 00:30:56 : How do we move memory around GPUs? Akshat 00:30:58 : Well, so sorry. Yeah, that is memory. Sorry, I was talking more, and maybe I was talking like five minutes back, about the private IPv6, addressing that you’ve set up. Swyx 00:31:09 : Yeah. Akshat 00:31:09 : Is it like it’s a VPN? Swyx 00:31:10 : Yeah, it is like a VPN, and yeah, WireGuard is, yeah, you’re right. It is, Akshat 00:31:16 : Right. Yeah, you already moved on to new topics Swyx 00:31:17 : A similar Akshat 00:31:18 : Okay Swyx 00:31:19 : In the same space, WireGuard is, encrypted and this is, Akshat 00:31:23 : And you don’t need encryption. Swyx 00:31:23 : Yeah. Akshat 00:31:24 : Yeah. Swyx 00:31:24 : This is not encrypted. that’s the main difference. This is TCP and we have eBPF programs that will reject or allow the TCP connection based on whether you’re allowed to do it. Akshat 00:31:35 : Used to involve a full sidecar, but now you have eBPF in the Linux kernel. Swyx 00:31:39 : Yeah. Akshat 00:31:40 : Yeah. I don’t know if this is a natural follow-on to the topic of like my skepticism on distributed training is that while, like, people spend a lot of money on, like, cables to hook up GPUs, and even that is not, like, fast enough, and that’s the bottleneck, is your networking fast enough? Swyx 00:31:59 : Yeah. So I guess you’re talking about fully distributed training like, Dialog or something which is like cross data center Akshat 00:32:06 : That would be, yes. Swyx 00:32:07 : That’s the extreme. Akshat 00:32:08 : Yeah. Swyx 00:32:08 : You’re in the middle, and then other people would have like the Mellanox cables up in, like, their actual data center. Akshat 00:32:14 : When you run multi-node training on Modal, RDMA, I think Mellanox, is, or InfiniBand is like a, is all seen as RDMA. but it’s a way to bypass the TCP networking stack and, transfer, stuff much faster, between one node, to the other. And we have I think like 3 terabit per second, internal networking Swyx 00:32:40 : Okay Akshat 00:32:40 : Which is the standard that’s needed. Swyx 00:32:42 : Okay. So I misunderstood what Akshat 00:32:43 : 50 Swyx 00:32:43 : What part of the stack you were Akshat 00:32:44 : 50 gigs over Swyx 00:32:45 : Yeah Akshat 00:32:45 : If you went Swyx 00:32:45 : Yeah Akshat 00:32:46 : RDMA. Swyx 00:32:46 : Okay. Swyx 00:32:48 : Yeah. I, very impressive work. Multi-Node Training, Post-Training, and Auto Research Swyx 00:32:52 : So effectively you’re extending like the model philosophy to the training cluster, like, yeah. Akshat 00:32:59 : Yeah. And we’re, we’re not going for like large scale training runs. the thing that we’ve built multi-node training for is, we see a lot of, smaller scale post-training. like, people are post-training like medium sized fund models, so they can, get higher quality on inference. this is a perfect fit, for something like that. Swyx 00:33:21 : Yeah. That is my impression of how a lot of these labs explore branches in post-training and then eventually merge whatever they find in. Akshat 00:33:31 : Yeah. The other use case we’ve seen for multi-node training is even if you have a big cluster, your researchers are still doing small runs Swyx 00:33:38 : Yes Akshat 00:33:39 : Having elasticity there Swyx 00:33:40 : Right, sure Akshat 00:33:40 : Matters a lot more. Swyx 00:33:41 : Yeah. the, like, this is like the current limiting factor for auto research, which is like you need to give your model some GPUs in order for it to completely run. Akshat 00:33:51 : We have a blog post on auto resource and model is, Swyx 00:33:55 : Yeah Akshat 00:33:56 : Yeah, like, turns out to be pretty good substrate for that. Swyx 00:33:59 : So my impression is auto research means many things, like Akshat 00:34:01 : Yeah Swyx 00:34:01 : Anything that Andrej coins. Right now it’s still science fair, right? Like not like, I don’t know how many people are doing this. Akshat 00:34:08 : We’re having a golf. Swyx 00:34:08 : Yeah. Akshat 00:34:09 : I thought the same thing. Swyx 00:34:11 : Yeah, you would know. Akshat 00:34:12 : We, like, our internal both training and inference teams use this the general shape of this quite a bit. like we have this one internal repo called auto inference, which essentially we’ve automated our own forward-deployed engineering efforts using, this harness, which is, the agent will just spin up a sweep of different things. It’ll even run like, NVIDIA inside profiler and it’ll like tweak configs and it’ll arrive the right thing. it’ll change your GPUs both from H200 to B200, and works really well. Swyx 00:34:47 : Nice. Akshat 00:34:47 : So yeah. Swyx 00:34:48 : By the way, I enjoy that your forward-deployed engineering is so technical that you have to do these things. Swyx 00:34:52 : It’s very different from forward-deployed engineering from other people. Akshat 00:34:54 : Yeah. For our forward-deployed engineering team is, essentially they’re like applied inference researchers or applied training researchers. Swyx 00:35:02 : Someone told me like they have to be able to build, but they also have to be able to sell. do they have to sell or are they like they’re good, they’re just like post-sale type of thing? Akshat 00:35:09 : It does, being able to talk to a customer and engage effectively with them Swyx 00:35:13 : Yeah Akshat 00:35:13 : Matters a lot. Swyx 00:35:14 : They want the same thing. Akshat 00:35:15 : Yeah. Swyx 00:35:15 : ? Akshat 00:35:15 : But it’s it’s not really a sales, thing. We pair them with-- We have solution architects as well that are more on the sales side. Swyx 00:35:23 : Okay. Let’s spend a bit more time on auto research. This is a big focus for for this year. Where does this go? like, have people explored enough? Like, there’s all these beautiful charts of like improve and then level off a bit and then you find the next thing. Is this one abstraction up from normal training? Is that how we think about it, or do you think about it differently? Like model level training versus high, like driven hyperparameter search. Auto Inference and Modal Bench Akshat 00:35:51 : Yeah, like, Swyx 00:35:51 : Someone, some people call it like neural architecture search or whatever, right? Like. Akshat 00:35:54 : Yeah, - So the stuff I’ve seen people do with it is nowhere on the architecture level. It’s pretty much tweaking parameters, but it’s it’s a hyperparameter sweep that’s guided by some model intuition, so it’s like much more efficient than, whatever other, sweep you would have. Swyx 00:36:12 : Yeah, it’s just, it’s just a question of where you want to spend your compute? Akshat 00:36:16 : Right. Swyx 00:36:16 : ‘Cause yeah, you can just throw infinite amounts of money on this and somehow you’ll bang out Shakespeare? Akshat 00:36:22 : Yeah, infinite monkey. Swyx 00:36:24 : Yeah, so like the very good for model. and I think it’s also very important that agents can spin up other agents, can spin up their infrastructure. Like very good for you. how good is our LLMs at generating model code? Like the benefit of existing LLMs is that you are in the data. Akshat 00:36:42 : Yeah. They’re, they’re surprisingly good. I think like pre Cloud 4 they were not, and then now they’re able to shot, stuff out of the box. But we’re playing around with releasing like a Modal Bench for like the harder Swyx 00:36:55 : Yeah Akshat 00:36:55 : Things, that the LLMs cannot do yet and maybe Swyx 00:36:59 : What’s an example of that? Akshat 00:37:01 : I think the things that- Sometimes agents struggle with, without right guidance and a skill is, how to, use the rest of our observability. Like how to. Something is failing, like how do you look at the logs and then update the right thing? It’s reasoning about that. But they’re able to shot, like Swyx 00:37:23 : Yeah. You can just add a skill to it? Compute Strategy and Capacity Planning Akshat 00:37:26 : Yeah. So we have a Modal skill now that. Which is why we built this Modal Bench. It’s to find things like that, so we can address them in our tool. Swyx 00:37:35 : Tune a skill. Yeah. Akshat 00:37:36 : Yeah. Swyx 00:37:36 : No. it’s it’s good. are you facing any shortages? like we talk a lot about GPU shortages, but also CPU, also memory. Swyx 00:37:44 : Yeah. Akshat 00:37:45 : We have had a lot of growth, which means that, there’s - we’ve had to be much better about Swyx 00:37:53 : Planning Akshat 00:37:54 : Proactive capacity planning. Swyx 00:37:55 : Yeah. Akshat 00:37:55 : So we have, Swyx 00:37:57 : Which by the way, like it’s like a MBA’s like dream Akshat 00:38:00 : Yes Swyx 00:38:00 : Is like just planning this stuff. I think last time you and I talked about something maybe about this. Akshat 00:38:03 : Yeah. we have a really competent team of people that we call, The role is called compute strategy. so yeah, if anyone listening here or wants to work on that Swyx 00:38:13 : Compute strategy? Akshat 00:38:13 : Yeah. Swyx 00:38:14 : I think, Akshat 00:38:14 : I feel like, Swyx 00:38:15 : I think the normies call it FP&A or something. Akshat 00:38:18 : Well, it’s more It’s it’s not FP&A. It’s it’s There’s a lot of interesting financial questions of like what is the blend between one year and three-year reservations? how do we forecast our own capacity? how do we. especially since our capacity is very fungible across different GPU types and different regions, like you have to model a lot of it. and you also have to have an opinion on how the supply chain is gonna evolve, and then you have to like, take bets, Swyx 00:38:49 : Yeah Akshat 00:38:49 : Based on that. Swyx 00:38:50 : Tokenomics. Akshat 00:38:50 : Yeah. Swyx 00:38:51 : This is like probably a not a real point, but, I was trying to think about like what other industries. I was trying to think about like, we cannot be first to like these kinds of problems. Akshat 00:38:59 : Yeah. Swyx 00:39:00 : And what other industries have had this? And I was like, airlines with fuel and like they have to hedge their fuel and like, I think for a long time Southwest because they made like a hero fuel bet, they like were like super low cost because Akshat 00:39:12 : Oh Swyx 00:39:12 : Compared to everyone else. Akshat 00:39:14 : Yeah. I hadn’t thought about that. Vibhu 00:39:16 : We’re at a fun time too? Akshat 00:39:18 : Yeah. It’s. A lot of the compute business in general, for us is also about being very good about capacity management. That is how you have great unit, economics. but also over time it’s how you can unlock more value for customers. Like, one of the things we’re building now is like a way for customers to get, If they don’t care about latency, like get much cheaper pricing and they’ll get results back in like next 24 hours or something, like a batch tier essentially. Batch Tiers and Latency-Insensitive Workloads Swyx 00:39:47 : Yeah. Akshat 00:39:47 : And those are levers we have because we control the whole stack and scheduling and whatnot to give people a sufficient Swyx 00:39:53 : Yeah. I feel like they’re not as popular. Like those, like the Frontier Labs have all those APIs. They’re not as popular as they should be. Akshat 00:40:00 : The demand that we see for something like that is not for LLMs. although sometimes people wanna run evals and Swyx 00:40:08 : Okay Akshat 00:40:08 : Synthetic data prep and there it makes sense. Swyx 00:40:10 : Okay. Akshat 00:40:11 : But it’s from a lot of LLM companies, like people who are doing computational bio, like they have to run really big batch jobs and they don’t care about when they get it back. Swyx 00:40:22 : Yeah. And like they have a reasonable. It’s it’s also like a cousin to the stopping problem of like, will this finish in time? Akshat 00:40:30 : Yeah. You can bound it. Swyx 00:40:33 : Yeah. Akshat 00:40:33 : Like you can give people Swyx 00:40:34 : Yeah Akshat 00:40:34 : SLAs on it. Swyx 00:40:35 : Yeah. I think what’s, what’s interesting is like the next phase of model. Swyx 00:40:38 : Like what, do people expect from you, now that you’re established and you’re like well-known compute player among all these leading companies. You had an inference launch week, and we talked a little bit about the launches. like what else? Like what else should people know? What Modal Builds Next Akshat 00:40:55 : We are building primitives that make our users’ lives much easier. So, I think for example, with LLM inference, thousands more companies are gonna post-train their own models and, deploy open source models for inference. so we’re thinking a lot about what is the best product shape for that. And, that involves everything from our training gym to, then, endpoints that get frontier-level performance. again, but I haven’t talked to anyone. It looks somewhat different on other verticals. Like, we’re also seeing a lot of real-time, audio-video stuff in there, which is why like, we’re working on things like regional routing, with fallbacks. So you can get GPUs that are as close to users as possible. so you get like low latency for video streaming and whatnot. And then on the agent side, it’s, Akshat 00:41:52 : We’re still working very closely with our customers because stuff is changing so fast in terms of what they need. And, I think beyond sandboxes and persistent file systems, there’s a lot of other things people will need from this agent stack as they build production agents. So yeah, we’re thinking about those other things that fit in there. Swyx 00:42:13 : I want to ask what the other things are. Akshat 00:42:15 : Yeah. I probably should share right now. Swyx 00:42:17 : I think-- I think, okay, so, I do think a lot about the principal components of cloud, and you do talk about compute storage networking. Akshat 00:42:25 : Yeah. Swyx 00:42:25 : Because so far for me, it’s fine. so far for the. the first couple generations of cloud, it’s fine. What’s different, qualitatively different about agents that you need some new permission level? Like a lot of people, okay, and I’ll just kinda spew tokens at you until it like hopefully sparks something. Akshat 00:42:43 : Yeah. Swyx 00:42:44 : Like the new level now is whatever Claude Code does, which is dangerously scope permissions or like allow list by command or like whatever, right? And sometimes they’re like, “Well, okay, we have like this adaptive thinking mode where like, just trust me, bro. I will make the calls for you.” Is that it? like mediated permissions. Hard Guardrails vs. LLM-Mediated Permissions Vibhu 00:43:03 : Now you’re looping it with a goal and letting it roll. Akshat 00:43:06 : Yeah, I’m, I’m skeptical of LLM media permission for stuff that is at the sandbox level because you do want hard boundaries. Swyx 00:43:16 : Yeah. Akshat 00:43:16 : Otherwise, someone can exfiltrate stuff. Swyx 00:43:20 : But like Akshat 00:43:20 : Yeah Swyx 00:43:20 : Maybe that’s old school thinking. Maybe we’re the dinosaurs. Swyx 00:43:23 : Maybe the AI OS or the LLM OS is really the kernel is a goddamn LLM. Swyx 00:43:30 : Like it makes you feel uncomfortable. Akshat 00:43:31 : Yeah, I’m, I’m told Swyx 00:43:32 : But that’s what trusting the LLM is. Like imagine a spherical cow perfect LLM. Akshat 00:43:36 : Right. Swyx 00:43:37 : That it. Akshat 00:43:39 : Maybe. Swyx 00:43:41 : I wanna test the boundaries, right? Akshat 00:43:42 : Yeah. Swyx 00:43:42 : Like, and I don’t believe that, but I wanna see where I’m wrong ‘cause that’s, that’s the consensus. Akshat 00:43:49 : Yeah. I think you always need hard guardrails when you want, And you can pair those with softer guardrails, right? And that’s gonna be a lot of mediated. Managed Agents and Specialized Sandboxes Swyx 00:44:00 : There. I’ll also get you a end with a couple of your commentary on like the ecosystem outside of Modal. Manage agents. Everyone has one. Gemini, OpenAI, Claude, very useful for you, but also like it is their way of starting to edge into your space. Akshat 00:44:17 : Yeah. Swyx 00:44:17 : What’s going on? Akshat 00:44:19 : Yeah, we’re, very excited to partner with Anthropic and some of the other foundation labs, will not name who we’re also working with. the way we see it is the manage agent thing is a great place to start if you’re starting out building an agent and, But then when you get to, building something more production grade, like you’re a company that’s like Ramp that’s building their own, Ramp also runs their accounting agent on us, so their external-facing agent. You need a lot more control over, your compute primitive on things like, what sort - how do you persist different files that the agent has access to, and how do you snapshot and restore? How do you control the networking? maybe you want GPUs. When you get to that point, you kinda want, a specialized sandbox provider, that gives you those things, and that’s the role that we are trying to play. Swyx 00:45:15 : Yeah Akshat 00:45:16 : We don’t really have an opinion on the harness, whether it runs - it’s a cloud-managed agent, and you hook it up to Model Sandbox, or you run the harness in Model Sandbox. We’ll see where people converge with that. Swyx 00:45:26 : Yeah. Do you any opinions on like the meta harnesses, or just another layer on top of these things? Akshat 00:45:31 : You mean like the OpenPipe Swyx 00:45:33 : OpenPipe is one. I think Vercel had one, which I can’t remember the name of right now. Fredshot had one. and then, to me, most recently was Data Databricks that had Omnigen. All these are meta harness. Like it’s kinda pseudo agent cloud type things. Akshat 00:45:50 : I personally have not played around with them. Swyx 00:45:53 : Yeah. Akshat 00:45:53 : Build agents with them. Swyx 00:45:54 : Everything’s bullish Modal, as long as it consumes more infra. Akshat 00:45:57 : That’s why we’re focusing on the infra layer. It’s somewhere where our, relative competence is and, also it’s a hard problem to solve. Swyx 00:46:06 : Yeah. I will say like just generally reflecting on that, I don’t know if - if there’s other topics on Modal, but like just generally reflecting as an infra person, not as intense as you, but in that field, this has like been the most exciting time in infra. Like it was boring for a while, and you couldn’t really get people excited about data infrastructure. Like Eric would get on Data Console, everyone just watched the video and like say, “Look at how many sandboxes I can spin up,” and no one gave a crap. Why Infrastructure Became Exciting Again Akshat 00:46:39 : Yeah. Swyx 00:46:40 : And like now everyone gives a crap. Akshat 00:46:42 : That’s true. It is a very exciting time, and I think a lot of that’s driven by just the amount of scale all of this stuff needs. Swyx 00:46:50 : I think the, like a lot of your initiatives or a lot of your like product directions make sense in retrospect, which is like the best kind, but I wouldn’t necessarily have thought about it myself, which. Akshat 00:47:00 : We need the predictions. Swyx 00:47:02 : I think there’s a lot that you just don’t even see, right? Like you have the batch, you have the voice, you have the multimodal, but what else? Akshat 00:47:10 : What else is coming up for us Swyx 00:47:11 : Yeah. Where do you see things going? Akshat 00:47:13 : Yeah. I, in general Biotech, Robotics, and Non-LLM AI Workloads Akshat 00:47:15 : It’s it’s clear that there’s there’s a huge shift happening. I think one thing that’s not as obvious to people because LLM inference gets talked about so much and is also we work a lot of companies that are, doing things like drug discovery and computational bio, like the Chai Discoveries of the world. Big things are probably gonna happen there. we work a lot of robotics companies that are putting robots in like active deployments and getting good results out of them. Swyx 00:47:45 : Is there Air Gap Modal? Is there a version that is like prem air gapped whatever? Akshat 00:47:50 : No. We, Swyx 00:47:51 : You should cloud only. Akshat 00:47:51 : Yeah. Swyx 00:47:52 : Yeah. Okay. But yeah, so what you’re saying is like because you’re focused on primitives and they’re good primitives, you find use cases in all these kinds of things. Akshat 00:48:01 : Yeah. Swyx 00:48:01 : Probably diversifies you a little bit away from LMS all the time. Akshat 00:48:05 : Yeah, absolutely. We’re, we’- our goal isn’t to only serve the LLM inference market. Swyx 00:48:10 : There are a lot just on the website, the audio, Akshat 00:48:12 : Yeah. We said both on Swyx 00:48:14 : Computational bio images. Yeah, there’s a lot here. There’s QTA TTS, customizing. Oh, Chatterbox. there was customizing Whisper. Akshat 00:48:24 : Okay. Yeah. Swyx 00:48:25 : This screen reminds me of a fallen competitor, which Replicate. Model APIs vs. Differentiated AI Products Swyx 00:48:31 : What’s your postmortem on what happened? Akshat 00:48:34 : This is one thing we’ve stayed away from is providing an API for models because I think providing model APIs is some of it ends up serving like a really hobbyist market, which is much less sticky. Swyx 00:48:50 : Yeah. Akshat 00:48:50 : And we’ve always wanted to build for companies that are building products and need more flexibility that’s not just an API. Swyx 00:48:57 : Which you can build an API for a model and this is clearly what it is. But you - but what you’re saying, you can wrap it into a more fully functioning back end that you run. Akshat 00:49:06 : Yeah. So all of our examples, it’s not that spin up this model, here’s an API token, use it. They’re all code. Swyx 00:49:13 : Okay. Akshat 00:49:13 : And so the point is that this is just an example. Swyx 00:49:16 : Starter code. Akshat 00:49:17 : Yeah. But you can tweak it however you want. Swyx 00:49:20 : Yeah. Akshat 00:49:21 : And if you’re like a company building a product, like, computational bio whatnot, yeah. Swyx 00:49:26 : I guess I’m trying to tease out for listeners Akshat 00:49:28 : Yeah Swyx 00:49:28 : When does it stop becoming, oh, you’re just an API call and you’re just a wrapper on API to becoming what you call a product, right? Swyx 00:49:36 : Like, what is that layer? Like what-- Like, more lines of code, but like beyond that, what is the substance that people add that qualifies it to be something more? Akshat 00:49:46 : I think there’s a little bit of like a selection effect of like a lot of the companies who do wanna get deeper into that level are probably building something that’s more differentiated. And, I think, an example is like - with LLM inference, originally we, worked with companies that were building their own post-training frameworks or they were, - Ramp early in the day was training their own tokenizer and like swapping out the tokenizer in Llama and whatnot. I’m not saying that’s, that successful, in that case. But a better example is like, let’s say Suno. because Suno, does not use Modal for training. Swyx 00:50:26 : Mikey on the pod. Yeah. Akshat 00:50:27 : But they use Modal for all their inference and that’s because they have like a custom-- They have completely custom model architecture and that means that they have to be at the code level and tweak things that are not, just an API. Swyx 00:50:41 : It’s interesting as well, like we had, Ethan, most recently on the xAI Groq team make a prediction that like the next tier in video gen is not a better video model, it’s a better model or agent that orchestrates video models. Video Agents and Production Workflows Akshat 00:50:56 : Oh, interesting. Vibhu 00:50:56 : Language model backbone that can use tools Akshat 00:50:58 : Right Vibhu 00:50:59 : And write code. Akshat 00:51:00 : Like, yes, I can make my second video or my second video from Groq, but I want my minute video. Akshat 00:51:06 : And I’m not going there through normal video gen. Swyx 00:51:10 : Yeah, that’s interesting. I - So we have GPU sandboxes and recently have seen a few companies doing agents that do video manipulation or, Akshat 00:51:22 : Yeah. Give it FFmpeg and just do it. Swyx 00:51:23 : Run FFmpeg. But like Akshat 00:51:25 : That’s not enough. Swyx 00:51:25 : Yeah. Akshat 00:51:26 : You need to give it Adobe. Swyx 00:51:27 : Yeah, I hadn’t put it together with like it would be a video production thing. in my mind these things were going more towards editing Akshat 00:51:36 : Yeah. Vibhu 00:51:36 : Well, shout out Mantis. Akshat 00:51:37 : I think about this a lot. Swyx 00:51:38 : . Akshat 00:51:41 : Yeah. Sorry. Vibhu 00:51:41 : Luma. Luma Agent is a version of this for video production, but it’s a off. Swyx 00:51:46 : I was gonna get your quick takes, on some other stuff that happens Gitpod/Ona, CI, and Runtime Sandboxes Swyx 00:51:50 : In recent news and just-just see if you have anything interesting. Gitpod, very like-- somewhat like, different market. They’re in like the CI/CD market, but technically very impressive. I don’t know if you’ve like taken a real look at them. Akshat 00:52:03 : Yeah. we’ve, - People on our team have talked to the Gitpod team and they’- they’re technically very strong. Swyx 00:52:10 : Yeah. Akshat 00:52:10 : I - We’re, we’re very bullish at Modal on the CI market as well because Swyx 00:52:15 : Okay Akshat 00:52:15 : There’s, there’s more agents, coding agents. Swyx 00:52:18 : Yeah. Akshat 00:52:19 : They’re gonna run a lot more CI and the primitives there can be much better. Swyx 00:52:23 : I think there’s a lot of wasted CI. Akshat 00:52:25 : Yeah. Swyx 00:52:25 : So is it just like let’s filter? Like what is the highest order bid here in improving CI for agents? Akshat 00:52:32 : Well, there’s a lot of wasted time in CI on like Swyx 00:52:36 : Preparing Akshat 00:52:36 : Preparing your artifacts and like, getting you to the preparing your dependencies and whatnot. Swyx 00:52:44 : Oh. Akshat 00:52:44 : And, like build systems help with that. But like if you have primitives that are like memory snapshot and restore, can you just run CI more efficiently? Swyx 00:52:55 : Oh, okay. Okay. Okay. Interesting. Yeah. another form of like, demand compute. Akshat 00:53:02 : Yeah, exactly. Swyx 00:53:03 : Yeah. Akshat 00:53:03 : It needs the same again, platform. Swyx 00:53:06 : Yeah. So, for those who don’t know, Gitpod rebranded to Ona. Swyx 00:53:09 : It was like there was this whole thing. I - I like semi-sounded the alarm at Cognition. I was like, “You should take these guys seriously because their infra is very good.” Akshat 00:53:17 : Yeah. Swyx 00:53:18 : And but, then they join OpenAI and, presumably we’ll, we’ll see Codex Cloud from the Ona team. Swyx 00:53:26 : Like which I think would be very strong. - To me, like teams like that can set up the networking and like the secure boundaries for like, and your like agents to have their own cloud each, effectively is what you’re doing and I’m just trying to draw the analogy or the differences if you have studied them. Like what is the philosophical difference? Akshat 00:53:47 : My sense is maybe they didn’t go after the right market at the right time because - I guess also got lucky with like agent use cases really taking off and, needing, like more of like a sandbox shaped thing than like, my understanding is, yeah, Gitpod Swyx 00:54:06 : Really sandboxes work Akshat 00:54:07 : Never mind Swyx 00:54:07 : Like CI/ Akshat 00:54:08 : Yeah Swyx 00:54:09 : Is sandboxes. Akshat 00:54:09 : Yeah. Swyx 00:54:10 : It’s just like build time sandboxes versus runtime sandboxes and it turned out runtime was better. Akshat 00:54:15 : Right. And the difference there is runtime sandboxes have a different configuration surface of like how you configure images, how you like attach like storage Swyx 00:54:25 : Yeah. It’s it’s fascinating. Other people, Astral also OpenAI. Python, TypeScript, and the Future of SDKs Swyx 00:54:30 : Also like Python tooling ecosystem people. Are you still bullish build- building on top of Python? Also recently Modular also got bought by Qualcomm. Just any of your takes there? Akshat 00:54:43 : Yeah. we had Python as our first SDK language because that was the language that people did data and ML in. I now have Go and TypeScript SDKs as well. and our runtime is completely language- It is written in Rust, but it’s it’s not tied to Python by any means. We haven’t seen-- I think with like inference and training stuff, people are still very Python and the interesting thing with like the agent stuff is people use our TypeScript SDK a lot more because they’re not doing anything that needs ML. Akshat 00:55:13 : I don’t think we’ll have to go beyond that super soon Swyx 00:55:16 : Yeah Akshat 00:55:16 : ‘cause Python and TypeScript is still Dominant. Swyx 00:55:19 : The last two languages in the world. Akshat 00:55:21 : Yeah. Swyx 00:55:21 : That’s it. Akshat 00:55:22 : Well, English and prompting is the fourth language. Swyx 00:55:25 : English and prompting. I occasionally talk to people who try to build new languages. They’re like, - Even, what’s his face? Brett Taylor, who’s chairman of OpenAI was like, “We need a new language for LLMs.” So no one has come across one, and I keep looking. Python and TypeScript - You have a lot of data plus, but then also they are very imperfect as just as languages themselves. Then my close is, I think Modal used to be a big bet on developer experience. Agent Experience as a Company-Building Wedge Swyx 00:55:52 : And you’ve pivoted the team to agent experience. Is it like the way now, like, do - do, - can entire companies and unicorns, multi-unicorns be built on just having better agent experience? Do you need something else? Akshat 00:56:05 : It’s a big part of our identity. it’s not just, like the very tactical, how does an agent use the CLI, but it’s also how easy is it to spin something up? Like, what is your iteration time when you wanna spin up a new service and, you wanna get something going in prod? in practice, that matters a lot, to people. And, I think it will continue to matter. Like, people are building stuff even faster, and if you give them ways to do it quickly not have overhead, then. Swyx 00:56:37 : I think the debate for me has been, do you do anything differently that is, like, very fundamentally different for developer experience versus agent experience? Swyx 00:56:44 : You seem to be on the side of they’re, they’re like this. They’re like cosine Akshat 00:56:48 : Yeah. We also have a blog post on that. Swyx 00:56:49 : Cosine similarity on, like, zero point nine or whatever. Akshat 00:56:53 : Yeah. pretty much it’s the main shift for us has been, as I said, like, we built this, benchmark, Modal Bench, to see where agents are lacking Swyx 00:57:02 : Yeah Akshat 00:57:02 : Literally add surface areas to a product if they’re reaching for something, like maybe this should just be a CLI. Swyx 00:57:09 : They halluc Oh, yeah. They hallucinate their own features. Akshat 00:57:11 : Yeah. And sometimes it makes sense. Like if they’re reaching for this thing, it’s product feedback. Like, give it to them. And then, yeah, moving-- we used to only have, like, logs and metrics in our UI, just moving all those things to the CLI as well, so they’re accessible in that form. Swyx 00:57:26 : Simple as that. Closing: Modal Bench, AX, and Execution Swyx 00:57:28 : Cool. Thank you so much. Yeah. Akshat 00:57:29 : Yeah. Thank you. Swyx 00:57:30 : This was great. Akshat 00:57:30 : This was fun. Swyx 00:57:30 : Yeah. It was a great update and, I can see why you guys have succeeded so much. it is really, focus, but also really good execution. Akshat 00:57:39 : Thanks. we have a long way to go. Swyx 00:57:41 : All right. Thank you. Akshat 00:57:42 : Cool.