AWS, Microsoft Azure, and Google Cloud risk losing the next phase of the AI market by charging too much for the same level of compute.

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Hyperscalers like AWS, Azure, and Google Cloud are charging 3–6x more for GPU compute than neocloud and specialized competitors, with NVIDIA H100-class instances costing ~$2/hr on Spheron vs ~$6.88/hr on AWS. As AI shifts from experimental to long-term operating expense, enterprises are increasingly scrutinizing unit costs and adopting multi-cloud workload placement strategies. The argument that hyperscaler ecosystems justify the premium is weakening as AI buyers grow more cost-rational. If the major cloud providers don't adjust their pricing strategies, they risk losing AI workloads to disciplined, lower-cost alternatives — not because of technical inferiority, but purely due to pricing practices.

The hyperscalers are pricing themselves out of AI workloads