Which platforms support both CPU and GPU workloads in your own VPC?

Running CPU and GPU workloads in an organization’s own VPC involves placing independently scalable application components, such as APIs, preprocessing workers, inference services, and training jobs, within customer-controlled cloud, Kubernetes, private-cloud, or bare-metal infrastructure rather than merely accessing vendor-hosted GPUs through private networking. The comparison identifies Northflank, Anyscale, and Red Hat OpenShift AI Self-Managed 3.5 as representative options with different operational scopes: Northflank targets complete production applications by managing CPU and GPU services, jobs, storage, networking, CI/CD, and observability through BYOC or eligible BYOK Kubernetes deployments; Anyscale targets Ray-based distributed computing with independently scaled CPU and GPU worker groups in a customer data plane while retaining a hosted control plane; and OpenShift AI extends customer-operated OpenShift environments with AI development, training, serving, and accelerator scheduling capabilities. Selection should consider where runtime workloads, data, logs, metadata, and control-plane services reside, as well as infrastructure support, scheduling behavior, GPU capacity, operational responsibilities, and security requirements. Organizations are advised to test representative end-to-end workflows, including scaling, failure recovery, networking, secrets, observability, and cost attribution, because BYOC does not necessarily keep all metadata or telemetry within the customer environment.

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