Use when deploying, automating, or troubleshooting Nebius AI Cloud or Token Factory with CLI, Terraform, APIs, Kubernetes, Slurm, Serverless AI, or OpenAI-compatible inference. Do NOT use for Nebius company, investor, or stock research.
Use when deploying, automating, or troubleshooting Nebius AI Cloud or Token Factory with CLI, Terraform, APIs, Kubernetes, Slurm, Serverless AI, or OpenAI-compatible inference. Do NOT use for Nebius company, investor, or stock research.
Nebius Platform
Use current Nebius documentation to design, provision, deploy, and operate workloads across Nebius AI Cloud and Nebius Token Factory. Prefer the smallest managed service that satisfies the workload, and make every infrastructure change reproducible, secure, observable, and removable.
When to Use
Use this skill for explicit Nebius work, including:
Nebius AI Cloud projects, IAM, VPC, Compute, GPU capacity, storage, registries, Kubernetes, Soperator, MLflow, Applications, Serverless AI, observability, or secrets.
Nebius Token Factory inference, embeddings, model discovery, fine-tuning, dedicated endpoints, or OpenAI-compatible API integration.
Migration, architecture, cost, security, reliability, and troubleshooting decisions specific to Nebius.
Do not use this skill for generic cloud work when Nebius is not selected, another cloud is named, local-only development, or Nebius corporate, investor, earnings, or stock analysis.
Inputs
Gather or infer as much of the following as the task requires:
Desired outcome and workload type: inference, training, batch, interactive service, cluster, storage, or platform automation.
Existing repository, manifests, container image, model, dataset, and deployment state.
Tenant, project, region, environment, naming conventions, and ownership labels.
A resource graph covering project, region, IAM, network, storage, compute, and workload dependencies.
Validation, observability, rollback, and teardown steps.
A concise record of files changed, resources created, unresolved constraints, and cost-sensitive choices.
Workflow
1. Route the request
Choose the service before writing commands. Read request routing.
Use Token Factory for managed model APIs, embeddings, fine-tuning, public serverless inference, or dedicated model endpoints.
Use Serverless AI to run the user's own container as an interactive endpoint or finite background job without managing a cluster.
Use Applications for supported turnkey software such as notebooks, model servers, and workflow tools.
Use Compute for full VM, GPU, driver, kernel, or custom runtime control.
Use Managed Kubernetes for long-lived, multi-service, portable container platforms.
Use Managed Soperator for Slurm-based distributed AI or HPC workloads.
Use Managed MLflow for experiment tracking and model registry workflows.
Do not confuse Token Factory with Serverless AI: Token Factory serves supported models through managed APIs; Serverless AI runs container images supplied by the user.
2. Establish live context
Before exact commands or architecture choices:
Inspect the repository and existing configuration.
Check current official documentation and the Nebius changelog.
Confirm the active project and region; use one CLI profile per project.
Check IAM permissions, quotas, and GPU capacity where relevant.
Discover current resource IDs, platform presets, model IDs, API fields, and versions instead of guessing them.
Use marketing pages for product orientation only. Use docs.nebius.com, docs.tokenfactory.nebius.com, official API references, and official Nebius repositories for implementation details.
3. Choose the control interface
Console: guided exploration or a one-off operation.
CLI: discovery, diagnostics, scripting, and small imperative changes.
Terraform: persistent or multi-resource infrastructure that must be reviewed, repeated, or promoted between environments.
OpenAI-compatible client: Token Factory inference and supported model operations.
kubectl/Helm: workloads inside Managed Kubernetes, not creation of the Nebius cluster itself.
Slurm tools: jobs inside an existing Soperator cluster.
Prefer Terraform for durable infrastructure. Use the CLI to inspect reality and to fill data needed by Terraform, not as an undocumented substitute for infrastructure as code.
4. Design the resource graph
Define dependencies in this order when applicable:
Tenant/project/region and ownership metadata.
IAM identities, groups, roles, and automation credentials.
Network, subnets, security boundaries, DNS, and public exposure.
Registry, object storage, disks, shared filesystems, and secrets.
Compute, Serverless AI, Kubernetes, Soperator, Applications, or Token Factory resources.
Workload configuration, autoscaling, health checks, logs, metrics, alerts, and lifecycle policy.
Never invent current GPU platforms, presets, regions, prices, quotas, capacity, model IDs, Kubernetes versions, or API fields. Query them at execution time.
Capacity-advisor output is a timestamped signal, not a guarantee that GPU capacity will still exist at creation time.
A Nebius project is region-specific. Keep project and region explicit in plans and outputs.
Use service accounts with least privilege for automation and user federation for interactive work unless requirements dictate otherwise.
Treat AI Cloud credentials and Token Factory API keys as separate credential domains.
Do not modify Managed Kubernetes worker VMs through Compute; manage them through Managed Kubernetes node groups.
For Serverless AI, use endpoints for interactive requests and jobs for finite background work.
For Token Factory, discover the available model catalog at runtime and use an environment-selected model ID.
Check quotas and capacity before provisioning GPUs; include cleanup guidance for every billable test resource.
Preserve user-owned infrastructure. Import, reference, or isolate existing resources instead of silently replacing them.
Quality Checklist
The request is explicitly Nebius-related and routed to the correct service.
Current official documentation or live discovery supports every dynamic value.
Tenant/project/region and credential mode are explicit.
Secrets are referenced safely and never exposed.
Quota, capacity, network exposure, data residency, and cost are considered.
Infrastructure is reproducible or the reason for an imperative approach is stated.
Validation covers resource state, workload behavior, logs, and metrics.
Rollback and teardown are included for risky or billable changes.
No placeholder is presented as a real ID, model, preset, price, or endpoint.
