CoreWeave Enables Faster Product Teams
Samiur Rahman, CEO of Heyday, on building a production-grade AI stack
CoreWeave’s edge here is not a faster model response for the end user, it is a faster product team. Heyday says an H100 on CoreWeave does not feel materially different from an H100 on Lambda Labs in raw speed. The advantage is that CoreWeave lets the team ship GPU backed features like public APIs, autoscaling, and Kubernetes based services without building that infrastructure layer themselves, which matters more than a few milliseconds at this stage.
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Heyday uses CoreWeave for all production ML compute, while using Lambda Labs for cheaper experimentation and training. The split is simple, Lambda is lower cost per GPU, but CoreWeave is closer to AWS for running live customer facing services.
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The concrete workflow difference is operational. On Lambda, Heyday would need to manage its own Kubernetes cluster and autoscaling. On CoreWeave, the team can move Docker based workloads over with minimal code changes and expose them to the public web or connect them into AWS VPCs.
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That positioning matches the broader market. CoreWeave has been building AWS like tooling around scarce GPUs and selling into companies that need to train, fine tune, and deploy models in production, while Lambda Labs has been more associated with flexible, developer oriented training workloads.
As GPU supply normalizes, the winning cloud will be the one that feels most like standard production infrastructure for AI teams. That pushes the market toward managed GPU clouds with networking, autoscaling, and reliability built in, and away from cheaper raw instances unless customers are large enough to operate that stack themselves.