Production AI workloads carry infrastructure requirements that conventional cloud sizing exercises miss: dense GPU clusters with low-latency interconnect for training, regional capacity for serving with predictable tail latency, fast object storage for embeddings and feature stores, and managed model endpoints with metered scaling. This ranking compares the 8 cloud platforms most often selected by enterprises running AI workloads at production scale, weighted toward accelerator availability, networking topology, model-serving primitives, and reserved-capacity economics rather than general-purpose cloud criteria.
AI workload selection rewards different criteria than general-purpose cloud selection. The four factors that determine fit are accelerator availability, networking topology, model-serving primitives, and unit economics under sustained load. Accelerator availability is the binding constraint at most enterprises in 2026: Nvidia H100 and H200 capacity remains rationed at the hyperscalers, and only reserved commitments guarantee allocation for multi-month training runs or large inference fleets. CoreWeave, Crusoe, and OCI typically have shorter wait times than AWS, Azure, or GCP for dedicated capacity.
Networking topology determines what kinds of training jobs are feasible. RDMA over Converged Ethernet or InfiniBand interconnect is essential for multi-node training above the eight-GPU threshold. AWS UltraClusters, GCP A3 Ultra, OCI Supercluster, and CoreWeave clusters meet this bar; conventional VM-on-VPC topologies do not. Model-serving primitives matter for production inference: managed endpoints with autoscaling, quantisation support, batching, and token-level metering reduce the operational burden compared with self-managed serving.
Unit economics under sustained load is where pilots most often fail to graduate to production. Reserved capacity discounts on hyperscalers can hit 70 percent against on-demand, and the gap between models on inference is at least 10x. Specialist platforms like Together AI and Fireworks consistently undercut hyperscaler per-token pricing on open models. For broader context, see the full cloud infrastructure directory, the AI and Machine Learning category, and our AWS vs Azure comparison.
| Product | Best for | Deployment | Rating | Starting price |
|---|---|---|---|---|
| Amazon Web Services | AWS-aligned enterprise AI | Hyperscaler | 4.4 | On-demand + Savings Plans |
| Microsoft Azure | Azure OpenAI and Microsoft estates | Hyperscaler | 4.3 | Reserved + on-demand |
| Google Cloud Platform | TPU training and Vertex AI | Hyperscaler | 4.3 | On-demand + CUD |
| CoreWeave | Large-cluster training | AI Cloud | 4.5 | Reserved + on-demand |
| Oracle Cloud Infrastructure | RDMA training, low egress | Hyperscaler | 4.2 | Reserved |
| Together AI | Open-model inference at scale | Inference | 4.5 | Per-token |
| Lambda Labs | Research and small training | AI Cloud | 4.4 | On-demand |
| Crusoe Cloud | Sustainable-compute AI | AI Cloud | 4.3 | Reserved |
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