How to Move GPU Inference from Lambda or CoreWeave to Your Own AWS or GCP Account (Without Rewriting the Pipeline)

Moving a containerized GPU inference workload from specialized providers such as Lambda Labs or CoreWeave to AWS, GCP, Azure, or Scaleway is usually an infrastructure change rather than a code rewrite, provided the image, CUDA stack, model artifacts, serving runtime, and API contract remain fixed. The main migration concerns are securing GPU quota and capacity early, selecting equivalent GPU hardware based on model and VRAM needs, ensuring compatible host drivers and Kubernetes NVIDIA device plugins, moving model weights to object storage, configuring startup probes for lengthy model loads, and preserving autoscaling and private networking behavior. The recommended process is to request quota first, validate the container and GPU node pool, deploy the same image digest, benchmark latency, throughput, utilization, and cost alongside the existing environment, then shift traffic gradually with a rollback period. Although specialized GPU clouds often offer lower raw hourly prices and faster access to new hardware, operating in a company’s own cloud account can improve total economics through committed-use discounts, reduced data-transfer costs, higher utilization, compliance control, and VPC-private data access. The piece also presents Qovery as a platform for managing Kubernetes-based deployments in customer-owned cloud accounts while retaining control of cloud billing and infrastructure choices.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.