7 best clouds for GPU-heavy AI inference in 2026 (and where OCI really fits)

A 2026 comparison of GPU-heavy AI inference clouds evaluates AWS, Google Cloud, Oracle Cloud Infrastructure (OCI), Azure, CoreWeave, Lambda, and Scaleway using GPU availability, normalized per-GPU pricing, egress charges, Kubernetes costs, inference tooling, and portability. AWS and Google Cloud are presented as strongest for organizations already invested in their data, IAM, compliance, and managed AI ecosystems, while AWS has the lowest listed H100 price among major hyperscalers at $6.88 per GPU-hour. GPU specialists offer lower raw H100 prices, including Lambda at $3.99, Nebius at $4.50, CoreWeave at $6.16, and Scaleway from €2.87, but generally provide fewer regions, services, and compliance capabilities. OCI is positioned as useful primarily for very high outbound traffic, due to 10 TB of free monthly egress and $0.0085/GB thereafter, or for bare-metal multi-GPU clusters with high-bandwidth RDMA networking; however, its $10-per-H100-GPU-hour price means it generally loses to AWS unless egress reaches roughly 350 TB per eight-GPU node each month or Oracle offers substantial committed-use discounts. Azure is suited to Microsoft-centered enterprises but has the highest listed H100 rate, while Scaleway, Nebius, and OVHcloud are highlighted as potential European residency options. The comparison emphasizes that data location, egress, capacity, operational complexity, and deployment portability can matter more than headline GPU prices, and it describes Kubernetes-based deployment layers such as Qovery as a way to reduce the operational burden of running inference across multiple clouds.

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