The outcome
Get into the official Nebius AI experience with the right account and a setup you understand.
Nebius AI is a AI cloud and model platform for managed inference, fine-tuning, serverless endpoints, and GPU infrastructure. Evaluating and serving open models through managed APIs or dedicated endpoints, and running controlled training and inference workloads on GPU VMs, containers, or Kubernetes. This guide narrows that broad capability into one repeatable outcome, with checkpoints that keep the source material and your judgment in the loop.
Before you begin
Set the boundary before the tool starts.
Choose one real task, identify who will use the result, and decide what evidence or test will make the result acceptable. Gather only the source material needed for that task. If the work contains confidential, personal, regulated, or client-owned information, confirm that the platform and account are approved before sharing it.
AI should make the work easier to inspect. If the workflow removes the source, the owner, or the review step, redesign the workflow.
Step by step
A workflow you can repeat.
- 01
Start at https://nebius.com/ and confirm the domain before entering account or payment information.
- 02
For desktop use, follow the official download link. Current availability: Nebius is a browser-managed cloud service with console, CLI, SDK, gRPC, and OpenAI-compatible model APIs rather than a consumer desktop application..
- 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.
- 04
Sign in with the account you intend to keep using, then review plan, data, notification, and permission settings.
- 05
Run one low-risk test task before connecting sensitive files, repositories, or workspace data.
Working standard
What good use looks like.
- Use official download pages.
- Review permissions during setup.
- Keep installers and applications updated.
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.
Official references
Check the current product documentation.
Features, plan limits, availability, and data controls change. These official pages are the starting points used for this collection.