The outcome

Run reproducible inference without exposing credentials, datasets, management surfaces, or unbounded cloud spend.

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.

Troiana principle

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.

  1. 01

    Define model and data rights, region, endpoint contract, GPU and memory needs, threat model, ingress, storage, scaling, observability, SLO, budget, and teardown evidence.

  2. 02

    Separate environments and roles, create scoped service credentials, restrict network access, encrypt storage, pin the image and model digest, and scan dependencies before deployment.

  3. 03

    Deploy privately with explicit health checks, timeouts, concurrency, replica bounds, secret mounts, non-sensitive logs, quotas, and no unnecessary management or shell exposure.

  4. 04

    Load-test cold start, queueing, autoscaling, memory, malformed input, model failure, key rotation, regional failure, output parity, privacy, and cost under representative traffic.

  5. 05

    Canary changes, retain a prior deployment and recovery manifest, export approved outputs, delete retired endpoints and disks, revoke credentials, and reconcile billing.

Working standard

What good use looks like.

  • Pin images and model revisions.
  • Restrict ingress and project roles.
  • Verify teardown and stopped billing.

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.