A dark corridor framed by tall red light columns, representing large-scale AI cloud infrastructure.
Nebius runs purpose-built AI factories: dense GPU clusters wired for training and inference at scale.

Nebius is an AI-focused cloud provider that rents GPU compute and managed AI infrastructure. It targets teams that train, fine-tune, and serve large models but do not want to build their own data centers or fight for capacity on a general-purpose hyperscaler. Nebius describes itself as a full-stack AI cloud, meaning it controls the layers from hardware and networking up through managed inference endpoints.

The company was created out of the former Yandex N.V. In 2024, Yandex N.V. sold its Russian assets and the remaining international business became Nebius Group N.V., headquartered in Amsterdam and listed on Nasdaq under the ticker NBIS. That history matters because the engineering teams behind Nebius operated large-scale infrastructure for years before the rebrand.

Where Nebius sits in the stack

Nebius owns and operates the physical layer rather than reselling someone else’s capacity. It exposes that capacity as bare GPU instances, managed Kubernetes, storage, and higher-level inference services, so you can enter the stack at whatever level of abstraction fits your team.

Managed AI services
Managed Inference Token Factory Serverless and dedicated model endpoints
Orchestration
Managed Kubernetes MLOps tooling Job scheduling and cluster management
Compute and storage
GPU instances AI storage NVIDIA GPUs, object and file storage
Physical infrastructure
Owned data centers InfiniBand networking AI factories in Europe and the US

Nebius is an NVIDIA Reference Platform Cloud Partner, and NVIDIA announced a strategic partnership and investment to help Nebius deploy NVIDIA systems at gigawatt scale. Its published data-center footprint spans Finland, France, Iceland, the United Kingdom, and multiple US sites including New Jersey and Missouri.

How to access it and typical use

You access Nebius through the Nebius AI Cloud console, its API, and a command-line client. There is no local install to run the platform: you provision resources in a region and connect to them over the network.

A typical training or serving flow looks like this.

Step 1 Provision Create a project and request a GPU cluster or a managed Kubernetes environment in a chosen region.
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Step 2 Load data Move datasets and checkpoints into Nebius AI storage close to the compute.
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Step 3 Train or fine-tune Run jobs across the GPU cluster using the InfiniBand fabric for multi-node scaling.
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Step 4 Serve Deploy the model to Managed Inference or your own endpoints and route production traffic.

Common workloads include pretraining and fine-tuning foundation models, batch and real-time inference, and rendering or simulation jobs that need many GPUs at once. Nebius names customers across media, robotics, and financial services. Its Token Factory product provides managed inference for open models, so teams that only want an API endpoint can skip the cluster management entirely.

If you want raw GPUs by the hour with minimal abstraction, Lambda Cloud covers that. If you want a serverless API for open models without touching infrastructure, Together AI is closer to that shape. Nebius spans both ends of that range.

How Nebius compares

NebiusCoreWeaveLambdaAmazon Bedrock
TypeFull-stack AI cloudGPU-first AI cloudGPU cloud and workstationsManaged model API
You manageCluster or serverlessClusterInstancesNothing, API only
Owns data centersYesYesPartlyUses AWS
Managed inferenceYes, Token FactoryYesLimitedYes, native
Home marketEurope and USUS-ledUS-ledGlobal
Best forTraining plus servingLarge GPU fleetsFast GPU accessNo-ops model calls

For a wider view of the model and provider landscape, see the 2026 LLM landscape .

When not to use it

Nebius is built for GPU-heavy AI work. It is not the right choice in several cases.

  • You only need a model API. If you want to call a hosted model and never think about infrastructure, a managed API like Amazon Bedrock or a serverless inference provider is simpler.
  • Your workload is not GPU-bound. Standard web apps, databases, and CPU services fit a general-purpose cloud better. Nebius is not a drop-in replacement for a full hyperscaler product catalog.
  • You need one vendor for everything. If your organisation already standardises on AWS, Azure, or Google Cloud for identity, networking, and compliance, adding a separate AI cloud adds integration work.
  • You need a specific region Nebius does not serve. Check the current region list before you commit, because data-residency rules may rule it out.

Further reading

  • What is inference? : why serving a trained model is a distinct cost and engineering problem.
  • CoreWeave : a GPU-first AI cloud and the closest direct competitor to Nebius.
  • Lambda Cloud : GPU instances aimed at fast, low-friction access.
  • Together AI : serverless inference for open models when you do not want to manage clusters.
  • The 2026 LLM landscape : how providers and models fit together.
  • Nebius : the official product site with current services and regions.
  • Nebius AI Cloud documentation : official docs covering compute, storage, and inference.

Sources