How to use
Nebius AI.

1. 01

   Start at https://nebius.com/ and confirm the domain before entering account or payment information.

2. 02

   Availability: Nebius is a browser-managed cloud service with console, CLI, SDK, gRPC, and OpenAI-compatible model APIs rather than a consumer desktop application.

3. 03

   A Nebius tenant and project, scoped credentials, approved region and data path, a licensed model, representative evaluation data, budget limits, and cloud security and operations ownership.

4. 04

   Sign in with the account you intend to keep using, then review plan, data, notification, and permission settings.

5. 05

   Run one low-risk test task before connecting sensitive files, repositories, or workspace data.

• Use official download pages.
• Review permissions during setup.
• Keep installers and applications updated.

1. 01

   Define the task, data class, region, model license, quality, safety, latency, context, throughput, retention, availability, cost, and rollback requirements.

2. 02

   Choose Token Factory or another managed endpoint for API inference, Serverless AI for a custom container, or GPU infrastructure only when the added operational control is justified.

3. 03

   Create least-privilege project credentials, pin the model or container revision, and run a versioned benchmark with representative inputs, bounded outputs, usage, latency, and failure capture.

4. 04

   Review quality, safety, privacy path, license, performance, scaling, and total cost, then canary the selected deployment, monitor it, and retain a tested fallback and deletion plan.

• State the outcome before the background.
• Provide the real source material.
• Review the result before expanding the task.

1. 01

   Restate the intended end state and the blast radius before applying anything.

2. 02

   Review the generated configuration line by line against the provider's current documentation.

3. 03

   Apply to a non-production environment first and confirm the observed result matches the intended one.

4. 04

   Confirm the rollback path works by actually exercising it, not by assuming it exists.

5. 05

   Check cost, scaling limits, and network exposure before the change reaches production traffic.

• Test the rollback, do not assume it.
• Check what a configuration exposes to the public internet.
• Watch cost and rate limits as closely as correctness.

1. 01

   Use scoped, short-lived credentials, and never paste production secrets into a prompt or config file.

2. 02

   Confirm what each change exposes publicly before applying it, especially storage, databases, and admin endpoints.

3. 03

   Separate environments so a mistake in development cannot reach production data.

4. 04

   Set billing alerts and hard quotas before enabling autoscaling or usage-based services.

5. 05

   Review audit logs and revoke access for integrations that are no longer in use.

• OpenAI-compatible endpoints preserve familiar request shapes, not identical model behavior, moderation, licensing, or reliability. Treat cloud projects, secrets, networks, images, datasets, logs, and GPU resources as separate security boundaries; pin versions, restrict ingress and roles, encrypt data, set quotas, verify region and retention, and delete idle endpoints, disks, and credentials.
• Follow your organisation's approved-use policy.
• Never treat fluent output as authorization to act.

• Nebius AI Cloud documentation ↗
• Serverless AI model tutorial ↗
• Token Factory API introduction ↗
• Nebius inference API reference ↗

Working on something like this? See how we handle performance & infrastructure, or book a call.