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Deploy When Available is now GA
Queue for any GPU spec, even one that's fully rented out, and we'll deploy it the moment capacity opens up. No more refreshing the console or running a sniping tool.
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We raised a $100 million Series A. Here's what it means for you.

When Pardeep and I founded Runpod, we believed the developers building AI deserved a cloud built for that work, not replatforming tools borrowed from an earlier era of software. That conviction still runs through every decision we make.
Today marks a major milestone for Runpod. We’ve raised $100 million, led by Summit Partners, and we’ve crossed more than one million developers building on the platform.
The latter is the most real measure of the company we’re becoming. It’s the part of this announcement I’m most focused on.
Over the last two years, much of the market narrowed to a single part of the problem: hosted inference. Inference matters, and we run an enormous amount of it. Our Serverless platform has now handled more than ten billion requests.
But inference is one stage of a much longer process. Developers need to build and train, fine-tune on their own data, deploy to production, and scale when the work succeeds. Pods for development and training. Serverless for production inference and agentic workloads. Clusters for multi-node runs. One platform, carrying a developer from first experiment to production traffic. That is what we mean when we call Runpod the AI developer cloud.
Not long ago, a customer told us we'd become their AWS. They meant it literally: if Runpod goes down, they go down. That's not a compliment to take lightly. When someone builds their company on your infrastructure, there's only one acceptable answer: be there.
This round ensures we can. We're building the next layer of the AI developer cloud faster, pulling more of the lifecycle into one place. The people building on Runpod should be thinking about their models and their products, not infrastructure.
I also want to celebrate every person who built this: every engineer who shipped a release this year. Every member of our support team who treated a ticket as if the company depended on it, because it does. And every customer who told us plainly where we fell short and gave us the chance to make it right.
I'm grateful to our investors for understanding what we're building, and for backing the company we intend to become: one with real demand and real traction behind it.
And to every developer and every team that has built on Runpod so far: thank you.
Blog Posts
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Queue for any GPU spec, even one that's fully rented out, and we'll deploy it the moment capacity opens up. No more refreshing the console or running a sniping tool.

Explore why faster chips have shifted the bottleneck to AI infrastructure, and what that means for teams running production workloads.
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With MIG, we can partition RTX 6000 Pro cards into isolated 24 GB instances. Here's when it makes sense for your workloads.