Inference speed changes unit economics

This points to inference hardware becoming a real switching lever, not just a back end detail. For a product like Heyday, where users are waiting for search results and assistant responses, 5 to 10x more tokens per second at similar cost means shorter waits, more queries served per dollar, and less need to overbuy GPU capacity. That is why Groq matters differently from CoreWeave. CoreWeave mainly sells reliable access to Nvidia fleets, while Groq is trying to change the unit economics of the inference job itself.

• CoreWeave won early by making Nvidia scarce compute usable in production. It wrapped raw GPUs with the cloud basics AI teams need, like autoscaling, networking, virtual machines, and managed Kubernetes, which is why companies like Heyday could run live products there even if it was not as mature as AWS.
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• Groq is optimized for inference, not general GPU flexibility. Its pitch is that if the workload is mostly generating tokens from a model that already exists, custom chips can return tokens far faster than GPU clouds. Groq has published benchmark results showing 5 to 15x higher throughput on some open models, with low token pricing on GroqCloud.
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• The competitive split in AI infra is becoming clearer. CoreWeave and other GPU clouds are strongest when customers need broad Nvidia compatibility, large reserved clusters, or training and fine tuning workflows. Groq and similar custom silicon players are strongest when the bottleneck is serving model responses fast and cheaply at scale.
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The next phase of AI infrastructure will be won less by who has the most chips, and more by who matches the right chip to the right workload. If Groq keeps proving better speed per dollar on inference, products like Heyday will increasingly split training on Nvidia clouds and user facing generation on custom inference hardware.