CoreWeave production GPU infrastructure
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Samiur Rahman, CEO of Heyday, on building a production-grade AI stack
Lambda Labs is at least 50% cheaper per GPU, so we use it for certain things, but we don't use it for production because CoreWeave specializes as a Kubernetes cluster of GPU based machines.
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The real advantage is not cheaper GPUs, it is buying a GPU cloud that behaves like normal production infrastructure. CoreWeave fit Heyday because its GPU fleet could plug into the same Docker and Kubernetes workflow the team already used on AWS, with public endpoints, private networking, and autoscaling handled by the provider. That let Heyday ship inference reliably without building its own cluster management layer.
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This is the same split seen across the market. CoreWeave has focused on larger, production heavy customers that reserve big GPU clusters for live workloads, while Lambda has skewed toward more flexible, lower cost usage for smaller teams and non critical jobs.
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In practice, production means more than renting a GPU box. A team needs containers to start fast, traffic to route to the right model server, capacity to scale up with demand, and networking that connects GPU workloads back to the rest of the app. CoreWeave packaged more of that out of the box.
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The pricing gap reflects different products, not just different margins. CoreWeave scaled into a much larger business by selling managed, enterprise grade GPU capacity, reaching an estimated $1.9B in 2024 revenue versus Lambda at an estimated $425M annualized run rate at the end of 2024.
GPU cloud competition is moving the same way AWS once did, away from raw servers and toward managed infrastructure. As more AI products go from demo to always on software, the providers that win will be the ones that make GPU workloads feel boring, stable, and easy to operate inside an existing app stack.