CoreWeave Nvidia access advantage
CoreWeave
CoreWeave won early by turning Nvidia access into a simpler and cheaper way to get large GPU clusters online. In practice, that meant customers who could not get enough H100s from the big clouds, or did not want to pay hyperscaler prices, could rent ML specific Nvidia fleets from CoreWeave instead. That advantage was strongest when GPU supply was tight and when enterprises needed thousands of GPUs fast, not months later.
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Nvidia had a real incentive to help CoreWeave scale. CoreWeave was one of Nvidia's largest customers in 2023, Nvidia invested in the company, and industry reporting described Nvidia allocating scarce H100 supply to upstart GPU clouds like CoreWeave and Lambda as an alternative to AWS, Azure, and Google Cloud, which were also pushing custom chips.
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The price and availability edge showed up in customer behavior. One customer described AWS GPU options as the wrong cards and far too expensive before moving ML workloads to CoreWeave. The same interview said AWS H100 instances were 2 to 3 times the cost of CoreWeave, while TechCrunch compared A100 pricing and found CoreWeave below Azure and Google Cloud on the same GPU.
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CoreWeave was not just cheaper raw compute. It wrapped those GPUs in production features, like Kubernetes based clusters, autoscaling, networking, public APIs, and AWS VPC connectivity. That is why some teams used cheaper providers like Lambda for experiments, but paid more for CoreWeave in production. Microsoft even signed a multibillion dollar capacity deal to secure supply for OpenAI demand.
Going forward, this shifts from a supply arbitrage story to a platform and infrastructure story. As AWS, Azure, and Google catch up on Nvidia capacity and push their own AI chips, CoreWeave's edge will come from keeping the best Nvidia fleets available, locking in power and data center capacity, and making GPU clusters as easy to run as standard cloud infrastructure.