GPU and CPU compute
Run AI workloads on NVIDIA GPU systems spanning Blackwell and Hopper generations, or use Intel Xeon and AMD EPYC CPU instances for preprocessing, application backends, batch inference, evaluation, and automation.
Nebius AI Cloud
Freemium
Nebius AI Cloud is a purpose-built cloud platform for developing, training, and serving AI workloads. It combines GPU and CPU compute, managed Kubernetes, MLOps tooling, and managed or serverless inference for teams scaling from experiments to production.
What is Nebius AI Cloud?
Nebius AI Cloud is a purpose-built cloud platform for developing, training, evaluating, and serving AI workloads. It provides GPU and CPU infrastructure, cloud-native scaling, managed Kubernetes, MLOps tooling, and managed or serverless inference in one environment.
Teams can start with a single VM, create multi-node clusters, or scale to thousand-GPU environments. The platform emphasizes bare-metal-level performance through non-virtualized GPUs and network interfaces, while retaining on-demand and preemptible VM options and self-service cluster access.
What can Nebius AI Cloud do?
Non-virtualized accelerated infrastructure
Nebius virtual instances do not virtualize GPUs or network interfaces. Multi-node deployments can use an optimized, non-blocking NVIDIA Quantum-2 InfiniBand fabric for distributed workloads.
Flexible cluster scaling
Launch an individual VM or scale to a multi-node cluster, with on-demand and preemptible VMs, the ability to scale clusters up or down, and spare-node replacement.
Managed Kubernetes
Deploy and manage containerized AI workloads with a fully managed Kubernetes layer optimized for AI. Kubernetes is also available as a standalone service for teams requiring direct DevOps-level control.
AI development and inference services
Built-in MLOps tooling, repeatable cluster creation, self-service access, and managed or serverless inference support workflows from model development through production serving.
Use Cases
“Train and fine-tune large models”
Use multi-node NVIDIA GPU clusters and InfiniBand networking for large-scale language-model training, fine-tuning, mixture-of-experts workloads, and multimodal development.
“Build production inference systems”
Deploy managed or serverless inference for applications that need to serve models, while using GPU or CPU capacity according to latency and workload requirements.
“Run data and evaluation pipelines”
Keep GPU capacity focused on model work by running tokenization, feature engineering, data loading, document processing, bulk evaluation, and other batch tasks on CPU instances.
“Operate containerized AI applications”
Use managed Kubernetes for AI application backends, serving logic, orchestration layers, ML pipeline scripts, scheduled jobs, and CI/CD workflows.
Frequently Asked Questions
What kinds of workloads does Nebius AI Cloud support?
The supplied product information covers model training, fine-tuning, inference, data preprocessing, evaluation, AI application backends, orchestration, automation, and CI/CD workloads.
Can I use Nebius for both small experiments and large clusters?
Yes. Nebius describes launching a single VM as well as scaling to multi-node and thousand-GPU clusters. It also supports scaling clusters up or down and using on-demand or preemptible VMs.
Does Nebius provide Kubernetes?
Yes. Managed Kubernetes is described as a fully managed container orchestration layer optimized for AI workloads. A standalone managed Kubernetes service is also available for teams that need direct DevOps-level control over multi-node environments.
How do I get started or discuss capacity?
The site directs users to launch a first GPU instance in the Nebius console or contact the team about capacity, reserved pricing, or specific workload requirements.
Quick Facts
- Product type
- AI cloud infrastructure platform
- Primary workloads
- Training, fine-tuning, inference, evaluation, and AI application operations
- Compute
- NVIDIA GPU instances plus Intel Xeon and AMD EPYC CPU instances
- Orchestration
- Managed Kubernetes, with a standalone managed service option
- Scaling range
- Single VMs to multi-node and thousand-GPU clusters
- Pricing information
- Public rates and detailed billing terms were not available in the supplied sources
Nebius AI Cloud Alternatives
Hyperstack
www.hyperstack.cloud
Hyperstack is a cloud GPU platform for running AI and machine learning workloads, including training, inference, data analytics, and model development. It also provides AI Studio, virtual machines, and managed Kubernetes for deploying and operating GPU-backed workloads.
Together AI
together.ai
Together AI is an AI cloud platform for inference, fine-tuning, GPU clusters, sandboxes, and managed storage.
DeepInfra
deepinfra.com
DeepInfra provides hosted machine-learning model inference and on-demand GPU instances for developers and teams. Its catalog covers text, image, audio, video, embedding, reranking, and other model workloads with pay-as-you-go pricing.
Hyperbolic
hyperbolic.xyz
Hyperbolic is an open-access GPU and AI cloud for deploying on-demand H100, H200, B200, and other GPU capacity. It supports experimentation, training, fine-tuning, inference, and production workloads through on-demand instances, reserved clusters, and Private Cloud infrastructure.
Lambda
lambda.ai
Lambda provides cloud GPU compute for AI training, fine-tuning, inference, and prototyping. Teams can launch on-demand GPU instances, use production-ready 1-Click Clusters, or discuss reserved and single-tenant infrastructure for larger workloads.
CoreWeave
www.coreweave.com
CoreWeave is an AI-focused cloud platform that combines GPU infrastructure, storage, networking, orchestration, and operational tooling for training and serving AI workloads. It supports teams moving from model experiments to production systems, including reinforcement-learning and agent-development workflows.