Enterprises are signing multi-year AI infrastructure contracts before they have a clear picture of their options. The largest US cloud providers plan to raise capital expenditure by nearly 80% in 2026, power is now as scarce as capital, and whichever provider secures an enterprise’s GPU capacity today is likely to host its AI workloads for years. Yet most enterprises have barely evaluated the specialized AI clouds competing with hyperscalers for that business. In Futurum’s September 2026 AI Product Series survey, 8% to 19% of respondents did not know enough about a given specialized AI cloud to say whether they use it, compared with 1% to 2% for the largest hyperscalers.
Hourly GPU rates also understate the real differences between providers. A Signal65 total cost of ownership (TCO) and tokenomics study of a 5,000-GPU NVIDIA Blackwell deployment over three years found that CoreWeave cost less than three anonymized hyperscalers in six of seven configurations, and the gap widened once inference throughput was counted. On NVIDIA GB200 NVL72, a 22% three-year cost advantage became a 65% advantage in cost per million tokens.
In our latest thought leadership brief, Specialized AI Clouds Belong on the Shortlist for Multi-Year AI Commitments, completed in partnership with CoreWeave, Futurum Research examines why enterprises should price multi-year AI commitments on cost per token rather than hourly GPU rates, and why specialized AI clouds now belong on every shortlist for GPU-intensive work, alongside the hyperscalers rather than in their place.
In this brief, you will learn:
- Why scarce capacity and undecided buyers raise the stakes of today’s AI infrastructure contracts
- How cost per token reveals differences between providers that hourly GPU rates hide
- Where CoreWeave fits best: large-scale training and high-volume inference, where GPU costs dominate the bill
- How to run an evaluation that holds every provider to the same workload model and cost-per-token test
If you are interested in learning more, be sure to download your copy of Specialized AI Clouds Belong on the Shortlist for Multi-Year AI Commitments today.
Author Information
Nick Patience is VP and Practice Lead for AI Platforms at The Futurum Group. Nick is a thought leader on AI development, deployment, and adoption - an area he has researched for 25 years. Before Futurum, Nick was a Managing Analyst with S&P Global Market Intelligence, responsible for 451 Research’s coverage of Data, AI, Analytics, Information Security, and Risk. Nick became part of S&P Global through its 2019 acquisition of 451 Research, a pioneering analyst firm that Nick co-founded in 1999. He is a sought-after speaker and advisor, known for his expertise in the drivers of AI adoption, industry use cases, and the infrastructure behind its development and deployment. Nick also spent three years as a product marketing lead at Recommind (now part of OpenText), a machine learning-driven eDiscovery software company. Nick is based in London.
