The AI Cloud Stack: Where Hyperscalers and Neoclouds Actually Compete
This report continues our work on neoclouds, although we are coming at the question from a different angle. The last report focused on backlog quality, monetizable MW, customer concentration, contract duration, financing structure, and the way hyperscaler demand was turning into external infrastructure commitments. That still feels like the right starting point. The next step is a full competitive SWOT, which is a useful exercise for separating near-term capacity leverage from the harder question of platform durability.
Our thesis remains that neoclouds are a direct proxy for hyperscaler urgency. The hyperscalers are still the demand center, but the neoclouds give us a useful read-through because their backlog and financing structures help quantify how that urgency is being translated into external AI infrastructure demand. We continue to think this is the cleanest way to study the space. A large hyperscaler contract, a GPU-backed debt facility, a power reservation, or a multi-year take-or-pay structure tells us something about the pressure inside the cloud market even before that pressure fully appears in reported cloud revenue.
The report also puts some limits around the neocloud narrative. CoreWeave, Nebius, and IREN are each trying to become more cloud-like, and we believe those efforts are rational. CoreWeave still has the strongest neocloud software and orchestration layer today. Nebius is pushing in both directions, down into owned infrastructure and up into inference and agent software. IREN starts with the clearest ownership of the physical bottleneck, while the Mirantis acquisition gives it a more credible path to build managed GPU services above that power base. All three are taking steps beyond GPU rental, which they need to do if they want the market to value them as more than capacity intermediaries.
We would still be careful about treating them as future hyperscalers. The big three cloud platforms have breadth that took years to build: identity, security, governance, data platforms, developer services, application integration, global operations, support, compliance, and procurement relationships. That breadth is what makes them difficult to displace across the long tail of enterprise workloads. The neoclouds can be very good GPU clouds and have a meaningful role in AI factory capacity, while still facing a much harder path in competing for the broader enterprise cloud control plane.
That distinction is the central point of the report. Neoclouds are well positioned where capacity speed, GPU availability, financing creativity, and power access are the binding constraints. Those are real advantages in this phase of the cycle. Training demand, burst capacity, and frontier model infrastructure can move toward the provider that can deliver the most usable compute at the right time and price. That is where the neoclouds have earned their relevance. Whether those are long term differentiators is an area we explore in this report.
Production inference creates a different test. Once AI workloads move into enterprise deployment, customers start to care more about reliability, identity, governance, data proximity, application workflows, security posture, support maturity, and cost per token. That favors providers with deeper platforms. It also raises the importance of custom silicon in a cost per token or all you can eat world. We know from our CIO and CTO work that token cost is becoming one of the central considerations for agentic AI spend. AWS, Google, and Microsoft all have more infrastructure and software depth to apply against that problem, and AWS and Google in particular have more mature custom silicon paths through Trainium, and TPUs.
That is why the scorecard in the full report separates stack presence from business quality. Azure leads our stack-presence score because Microsoft owns enterprise distribution, identity, M365, Dynamics, OpenAI access, and Copilot pull-through. AWS remains the infrastructure trust and custom silicon benchmark. GCP remains the data gravity and TPU economics specialist. Oracle sits in the middle because OCI has credible bare-metal GPU and RDMA capacity, plus a real database and enterprise applications estate, although its AI software middle is thinner than the big three.
The neoclouds split by scarce asset. CoreWeave is the backlog and orchestration case. The bull case is revenue visibility and software depth, while the diligence work is customer concentration, lease duration, financing cost, and whether inference becomes a larger part of the mix. Nebius is the most hyperscaler-like of the group, with large Microsoft and Meta commitments, a push toward self-owned infrastructure, and software assets like Token Factory and Tavily. The test is whether those pieces become repeatable consumption economics. IREN is the power-first case. Its advantage is physical and harder to replicate, but the multiple depends on whether managed AI cloud services can turn that power base into recurring revenue.
The broader conclusion to us is that AI cloud demand is segmenting by workload. Training can follow price and availability. Production inference should stay closer to platforms with identity, data, reliability, and cost-control advantages. Regulated enterprise workloads will put more weight on governance and support. AI-native startups may continue to value speed, GPU access, and modern tooling before procurement standardization starts to matter. Power-constrained capacity has its own logic because the scarce input starts before the GPU cluster is deployed.
The full-stack map does not give us one winner. It gives us a better way to analyze and value the next phase of the cycle. For hyperscalers, the issue is whether distribution, custom silicon, data gravity, and enterprise trust turn AI demand into durable consumption. For neoclouds, the issue is whether power, orchestration, and capacity access remain scarce enough to support repeatable economics as hyperscaler self-supply catches up. We still think the neoclouds have an important role in AI factories. We also think the path from GPU cloud to full cloud platform is a much harder climb than the current backlog numbers alone suggest.
What subscribers get in the full report
A nine-layer full-stack framework for comparing cloud providers across power, data centers, custom silicon, GPU compute, networking, orchestration, model access, applications, and enterprise distribution.
A directional scorecard for AWS, Azure, GCP, Oracle OCI, CoreWeave, Nebius, and IREN.
Company-level SWOTs for each provider, with a specific diligence focus and read-through for every name.
A breakdown of why Azure leads through enterprise distribution, AWS through infrastructure trust and custom silicon, and GCP through data gravity and TPU economics.
A bridge-tier analysis of Oracle OCI and whether database and apps gravity can convert into AI workload attachment.
A neocloud comparison across CoreWeave, Nebius, and IREN, including backlog quality, power position, software depth, customer concentration, financing, and durability.
A workload map showing where training, production inference, regulated enterprise, data and analytics, AI-native startups, office agents, and power-constrained capacity are likely to route.
The central diligence question for the next phase: which AI cloud providers own a scarce layer that stays valuable after capacity becomes easier to procure.