Modal's Series C: Raising $355M at a $4.65B valuation
Announcing our $355M Series C Read more
All posts<br>Back News<br>May 21, 2026•3 minute read
Modal's Series C: Raising $355M at a $4.65B valuation<br>We’ve raised $355 million after growing fivefold since September, surpassing $300 million in annualized revenue. Our valuation is $4.65B post-money in a round led by General Catalyst and Redpoint, with Menlo, Bain Capital Ventures, and Accel joining as new investors. All our existing major investors participated as well, doubling down on their conviction in Modal.<br>A new infrastructure layer for the AI era<br>We started Modal because the cloud built for traditional web applications was never going to fit AI workloads. This was clear to us before the GenAI revolution and it becomes even more true as models and techniques advance.<br>Modal is a cloud built for AI. Not a single-purpose GPU cloud, but a platform with the right primitives for developers to build a very wide range of applications. Today, this looks like low-latency elastic inference, dynamic agent runtimes, reinforcement learning, batch jobs at massive scale, and much more.<br>Frontier APIs to model ownership<br>From digital-natives like DoorDash to AI-native companies like Reducto, the teams pulling ahead are taking ownership of their models. They're fine-tuning with their own data, running RL, and tuning inference for their own latency, throughput, and cost needs. Open-weight models from DeepSeek, Qwen and others have reached production quality, and inference engines like vLLM and SGLang have matured alongside them. For the first time, the full stack to own and serve your models is there, without sacrificing capability.<br>“Modal powers both our reinforcement learning infrastructure and production inference. Millions of sandboxes on one end, real-time serving on the other. All on the same platform. ”<br>— Scott Wu, CEO, Cognition
“Decagon was able to achieve a p90 latency of 342ms, well below the sub-second range required for natural customer conversations — delivering speed, efficiency, and enterprise-scale reliability”<br>— Research Team, Decagon
Agents need better execution environments<br>In 2023, we started seeing users run AI-generated code on Modal. It was clear this would become a universal need, so we built Sandboxes, isolated environments for untrusted code, as a first-class primitive. It took two years for the explosion to happen.<br>In the last six months, it's become clear: agents are going to be everywhere, and they're far more powerful when they have a runtime to operate in. DoorDash is building AI agents for merchants, coding agents like Ramp's Inspect author 70% of merged PRs, RL workloads run thousands of environments in parallel, and autoresearch agents run their own training experiments at scale. Over 1 billion sandboxes have been launched on Modal.<br>“Sandboxes are one of the most important building blocks for Reinforcement Learning. Out of everyone, Modal was clearly very flexible, structured in a way where we could build complex environments, really focused on performance and reliability.”<br>— Yash Patil, CEO, Applied Compute
“As we scale agentic commerce for local businesses, we need a highly efficient path to production with full harness control, scale, and reliability. We’re excited to evaluate Claude Managed Agents for this next step, building on our AI infrastructure with Modal.”<br>— Andy Fang, CTO, DoorDash
The shape of AI keeps expanding
Modal is a general compute platform built for the underlying needs of AI workloads: elastic compute, safe isolation, and programmatic control. Developers compose them into very different applications. Physical Intelligence runs real-time inference for live robots. Chai Discovery scales drug discovery pipelines from protein embeddings to antibody design. Suno generates millions of songs a day, scaling to thousands of GPUs and back to near-zero. Same primitives, completely different shapes.<br>“We use Modal to run edge inference with ”<br>— Brian Ichter, Co-founder, Physical Intelligence
“It’s not just a time savings, it’s the mental overhead that disappears. With Modal, we add a few decorators to a function we need to scale, forget about them, and they just work”<br>— Kevin Wu, ML Researcher, Chai Discovery
What we’re building next<br>We’ve spent the last five years going very deep on technology, including building our own storage and compute layer from the ground up. This has enabled us to achieve outcomes that seemed impossible, e.g. improve cold starts by 100x with GPU snapshotting, elastic low-latency inference globally, and scaling from 0 to 1,000 GPUs in minutes (or even seconds) without reservations by pooling capacity in hundreds of data centers all over the world.<br>Because we own the full stack, we can keep compounding those advantages to deliver a better and better experience for developers.<br>That foundation is what makes the next phase possible. Here's where we're going:<br>Low-latency inference at scale.<br>The bar for...