7 best ways to self-host AI inference on cloud GPUs in 2026 (GCP A5X, AWS and Scaleway compared)

Self-hosting AI inference on cloud GPUs can become cheaper than hosted per-token APIs only when sustained utilization is high enough to offset hourly GPU costs, with a gpt-oss-120B example showing break-even at roughly 33% utilization on Google Cloud Flex-start B200 pricing and 58% on AWS on-demand pricing. Google Cloud’s newly announced A5X bare-metal instances, based on NVIDIA Vera Rubin NVL72 systems, target frontier-scale workloads such as large mixture-of-experts models and long-context agentic serving, though detailed instance specifications, regions, and pricing were not yet public. The comparison finds that Scaleway offers the lowest listed H100 hourly rate, AWS provides extensive discount and capacity-purchasing options, and Google Cloud offers early access to newer NVIDIA hardware alongside committed-use and flexible scheduling discounts. Running production inference requires substantially more than renting GPUs, including Kubernetes GPU configuration, inference runtimes such as vLLM or SGLang, model storage, autoscaling, reliability practices, monitoring, security controls, and ongoing operational ownership. The recommended choice depends on traffic patterns, compliance needs, and platform expertise: raw Kubernetes stacks suit teams optimizing large, steady workloads, managed platform layers such as Qovery aim to reduce operational burden while retaining customer cloud accounts, and serverless or hosted providers such as Modal, Baseten, Vertex AI, Bedrock, and Together AI are presented as better fits for bursty, low-volume, or exploratory demand.

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