GCP Out of Capacity? How to Offload Workloads to AWS Without Running a Second Platform

Google Cloud capacity shortages in 2026 can affect CPU-based virtual machine families as well as GPUs, causing GKE autoscaling failures and pending pods through errors such as ZONE_RESOURCE_POOL_EXHAUSTED, which indicate unavailable physical hardware rather than insufficient quota. Google’s recommended responses include retrying, changing zones or machine types, using Spot or Flex-start capacity, reserving resources, and planning fallback options, while company statements and customer reports suggest supply constraints are broad and persistent. The piece presents AWS as a complementary capacity pool rather than an unlimited alternative, citing its larger infrastructure footprint, wide EC2 instance selection, and independent regions and supply chains, while noting that AWS also faces AI-related constraints. It recommends offloading portable workloads such as CI runners, batch processing, queue workers, preview environments, stateless APIs, and some GPU inference workloads, while retaining databases, data-intensive applications, and latency-sensitive services near their GCP data. It argues that the main obstacle to multicloud bursting is operational complexity across Kubernetes, identity, networking, deployment pipelines, and monitoring, and promotes Qovery as a platform for managing GKE and EKS environments through a shared control plane, allowing teams to clone and deploy environments across clouds while considering cross-cloud latency, data-egress costs, connectivity, and workload suitability.

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