From Execution Edge to Supply Moat
Samiur Rahman, CEO of Heyday, on building a production-grade AI stack
CoreWeave’s early edge came from execution, not lock in. For a customer like Heyday, the product is valuable because it turns scarce GPUs into a stable Kubernetes environment that can run training and inference jobs in production, but the workload still lives in containers and standard orchestration. That means the service feels sticky while supply is tight and reliability matters, yet it remains portable if AWS, Lambda, or another GPU cloud can offer similar uptime and price.
-
The practical wedge was production readiness. CoreWeave built AWS like features around GPUs, including Kubernetes based autoscaling, networking, and storage, at a time when many rivals mostly offered raw rented instances. That mattered for teams serving live user traffic, not just running experiments.
-
The tradeoff is that standardization lowers switching costs. Heyday describes its setup as Docker images on a Kubernetes cluster, and CoreWeave’s own platform emphasizes Kubernetes service, S3 compatible storage, and data mobility across clouds. Those choices help adoption, but they also make migration easier.
-
What can become a deeper moat is upstream access and scale. CoreWeave paired software with unusually strong Nvidia ties, large financing capacity, and huge committed contracts. That let it buy more GPUs faster than smaller clouds and turn supply advantage into revenue, from $229M in 2023 to $1.9B in 2024, while Lambda reached a $505M run rate by May 2025 and kept closing the feature gap.
Going forward, GPU clouds are likely to look more like regular cloud infrastructure. As rivals catch up on Kubernetes, scheduling, and reliability, advantage shifts toward who can secure power, chips, and long duration contracts at scale, then layer proprietary software on top. The next moat is less about being first to offer GPU clusters, and more about owning the supply chain and operating system around them.