Cloud Run GPU vs a dedicated GKE node pool: 7 ways to self-host an open-weight model in 2026

For self-hosting open-weight AI models in 2026, Google Cloud Run with GPUs is positioned for bursty or intermittent inference because it scales to zero, bills per second, and requires little infrastructure management, while dedicated Google Kubernetes Engine GPU node pools are better suited to sustained workloads, advanced hardware requirements, and multi-GPU serving. Cloud Run supports one NVIDIA L4 or RTX PRO 6000 Blackwell GPU per instance, but lacks A100/H100 options, multi-GPU replicas, MIG partitioning, and Spot or committed-use discounts; its cold starts are primarily determined by model-weight loading rather than instance startup. Based on cited us-central1 prices, Cloud Run is estimated to be less expensive when GPU instances run below roughly 30–40% of the month compared with Spot or one-year committed GKE L4 nodes, while GKE becomes more cost-effective at higher utilization despite its operational overhead. The comparison also covers GKE Autopilot, Qovery-managed Kubernetes, AWS SageMaker and EKS, Modal, and raw GPU providers such as RunPod and Lambda Labs, emphasizing trade-offs involving cost, data residency, hardware availability, operational responsibility, and cloud-account control.

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