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Serverless AI is a Nebius AI Cloud service for running containerized AI workloads without creating or operating virtual machines (VMs) or clusters. To run your workload in Serverless AI, you just need to choose how to deploy it (as an interactive endpoint or as a non-interactive job), specify the path to your container and select the computing and storage resources that the workload requires. Serverless AI handles resource provisioning and lifecycle: endpoints and jobs run on Compute container VMs, with usage-based, per-second billing. The underlying VMs are managed by Serverless AI. You can connect to them via SSH, but they aren’t shown as Compute resources and can’t be managed or modified directly like other Compute VMs.

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Endpoints and jobs

You can deploy your workload as an endpoint that listens for requests and returns results immediately, or as a job that runs in the background and quits after completing its task. Here is the comparison between endpoints and jobs at a glance:

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Observability and debugging

Each Serverless AI endpoint and job has a status that indicates the current stage in the lifecycle. You can view its status and logs on the resource page under Jobs or Endpoints in the web console. Endpoints and jobs also report GPU and vCPU utilization metrics on the Metrics tab of this page. To investigate errors or unexpected outcomes, see Debugging failed jobs and endpoints. For metric definitions and details, see Monitoring endpoints and jobs.

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Pricing and quotas

Serverless AI follows Compute billing and quota rules. Billing is usage-based: the service charges you per-second for the computing and storage resources that you allocate to endpoints and jobs. Only active endpoints and jobs are billed and count towards quotas. This can help you avoid unnecessary costs compared to always-on infrastructure. For more details, see Pricing and quotas.