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Nebius AI Cloud

Cloud infrastructure platform designed for AI workloads, offering scalable GPU clusters with NVIDIA hardware and optimized orchestration for training and inference.

Starting atOn-demand from ~$2.50/GPU/hr
Visit Nebius AI Cloud →
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In Plain English

Cloud infrastructure platform designed for AI workloads, offering scalable GPU clusters with NVIDIA hardware and optimized orchestration for training and inference.

OverviewFeaturesPricingUse CasesLimitationsFAQAlternatives

Overview

Nebius AI Cloud is an Infrastructure platform that delivers GPU-accelerated compute for AI training and inference workloads, with pricing available through pay-as-you-go and reserved contracts (contact sales for personalized quotes). It targets ML engineering teams, AI research labs, and startups building foundation models or large-scale inference pipelines.

Based on our analysis of 870+ AI tools in the AI Tools Atlas directory, Nebius stands out in the Infrastructure category by combining full-stack control over its own hardware with elevated NVIDIA partnership status — Nebius holds Reference Platform NVIDIA Cloud Partner status, a tier reserved for providers operating large clusters aligned to NVIDIA's tested reference architecture. The platform provides access to the latest NVIDIA GPU generations including GB300 NVL72, GB200 NVL72, B300, B200, H200 and H100 accelerators, interconnected via NVIDIA InfiniBand and Quantum-X800 InfiniBand networking. Nebius operates ISEG, ranked the #19 most powerful supercomputer in the world, located 60 kilometers from Helsinki, Finland, and has designed its own servers and racks to optimize each layer of the stack for AI workloads.

Customers span gene-editing research (CRISPR-GPT, developed by scientists from Stanford, Princeton and Google DeepMind, achieving 80-90% first-attempt efficiency for novice researchers), privacy-focused web search (Brave Software serves 1.3 billion search queries per month and delivers 11M+ AI-generated answers daily using Nebius with nearly 100% compute utilization), open-source LLM serving (the Linux Foundation's vLLM project uses Nebius clusters to optimize DeepSeek R1 inference), generative design (Recraft trained a 20B-parameter foundation model scoring 54% preference over Midjourney v6 on PartiPrompts), AI music generation (Wubble), and quantum chemistry for drug discovery (Simulacra AI, Quantori). Compared to other Infrastructure providers in our directory such as Lambda Labs, CoreWeave, and RunPod, Nebius differentiates through its EU-based data centers with strict compliance support, managed Kubernetes and Slurm orchestration, fully managed services (MLflow, PostgreSQL, Apache Spark), Terraform/API/CLI cloud-native tooling, and 24/7 solution architect support offered free of charge — CentML reported 5x lower costs compared to other major providers after migrating workloads to Nebius.

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Key Features

Latest-generation NVIDIA GPU fleet+

Nebius offers GB300 NVL72, GB200 NVL72, B300, B200, H200, and H100 Tensor Core GPUs, all interconnected with NVIDIA InfiniBand and Quantum-X800 InfiniBand networking. As a Reference Platform NVIDIA Cloud Partner, the clusters are built to NVIDIA's tested and optimized reference architecture, so performance is predictable across multi-node runs.

Managed Kubernetes and Slurm orchestration+

You can orchestrate thousands of GPUs in a single cluster using Managed Kubernetes or Slurm-based clusters, paired with fast storage. This gives teams the choice between modern cloud-native orchestration and traditional HPC scheduling without maintaining either control plane themselves.

Fully managed data and MLOps services+

Nebius provides zero-effort managed deployments of MLflow, PostgreSQL, and Apache Spark alongside compute. Teams can track experiments, store metadata, and run distributed data preparation jobs without operating the supporting infrastructure.

Cloud-native infrastructure-as-code+

The platform is fully manageable through Terraform, API, and CLI, with an intuitive web console for interactive work. Ready-to-go Terraform recipes and tutorials accelerate setup of common patterns like distributed training clusters or inference deployments.

Architect-led expert support included at no extra cost+

Every customer gets 24/7 expert support and dedicated solution architect assistance for multi-node workloads free of charge. An in-house AI R&D team dogfoods the platform, which means engineering decisions are informed by real ML practitioner pain points rather than theoretical benchmarks.

Pricing Plans

NVIDIA H100 SXM (80 GB)

On-demand from ~$2.50/GPU/hr

  • ✓80 GB HBM3 memory per GPU
  • ✓InfiniBand interconnect available
  • ✓Scale from 1 to 1000+ GPUs
  • ✓Pre-configured CUDA and drivers
  • ✓Managed Kubernetes or Slurm orchestration

NVIDIA H200 SXM (141 GB)

On-demand from ~$3.50/GPU/hr

  • ✓141 GB HBM3e memory per GPU
  • ✓InfiniBand interconnect available
  • ✓Optimized for large-model training and inference
  • ✓Managed Kubernetes or Slurm orchestration
  • ✓24/7 solution architect support included

NVIDIA B200 / B300

Contact sales for current pricing

  • ✓Latest Blackwell-generation GPUs
  • ✓Quantum-X800 InfiniBand networking
  • ✓Multi-thousand GPU cluster support
  • ✓Reference Platform NVIDIA architecture
  • ✓Dedicated solution architect engagement

NVIDIA GB200 NVL72 / GB300 NVL72

Contact sales for current pricing

  • ✓Grace-Blackwell superchip racks (72 GPUs per rack)
  • ✓Highest-performance training and inference configuration
  • ✓Quantum-X800 InfiniBand fabric
  • ✓Full-stack Nebius-designed server hardware
  • ✓Priority onboarding and architect-led deployment

Reserved Capacity Contracts

Custom pricing (significant discounts vs. on-demand; CentML reported 5x savings vs. hyperscalers)

  • ✓Committed-use discounts on any GPU SKU
  • ✓Guaranteed capacity allocation
  • ✓Dedicated cluster provisioning within ~1 week
  • ✓24/7 architect support and SLA included
  • ✓Flexible contract terms — contact sales for quote
See Full Pricing →Free vs Paid →Is it worth it? →

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Best Use Cases

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Training large foundation models from scratch — Recraft used Nebius with PyTorch + Kubeflow + NCCL to train a 20B-parameter generative design model that scored 54% preference over Midjourney v6 on the PartiPrompts benchmark

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Serving high-volume LLM inference in production — Brave Search runs 10–70B parameter models on Nebius to deliver 11M+ AI-generated answers daily at nearly 100% compute utilization

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Optimizing open-source inference frameworks — the Linux Foundation's vLLM project uses Nebius clusters to benchmark and optimize transformer inference including DeepSeek R1

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Life sciences and drug discovery research — Simulacra AI and Quantori run quantum-chemistry molecular generation pipelines, with Simulacra reporting 90% faster pre-training compilation (minutes vs. 2+ hours)

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Cost-optimized AI deployment platforms for third-party SaaS — CentML powers its open-source model deployment service on Nebius, achieving 5x lower cost and getting new clusters online within one week

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Multimodal and generative media workloads — Wubble uses Nebius and Kubernetes for scalable AI music generation across 100+ genres, reducing time to first token to 1.8 seconds

Limitations & What It Can't Do

We believe in transparent reviews. Here's what Nebius AI Cloud doesn't handle well:

  • ⚠Primary data center footprint is concentrated in Europe, limiting options for customers requiring Asia-Pacific or Latin America residency
  • ⚠Homepage does not display full per-GPU hourly pricing publicly; sales contact required for customized quotes and reserved capacity
  • ⚠Service scope is AI-specific — no broad PaaS, serverless functions, or consumer-facing services comparable to AWS or GCP catalogs
  • ⚠Operating multi-node InfiniBand clusters and Slurm workloads assumes meaningful ML engineering expertise
  • ⚠Third-party marketplace and ecosystem integrations are narrower than hyperscaler equivalents

Pros & Cons

✓ Pros

  • ✓Reference Platform NVIDIA Cloud Partner status — a tier reserved for select partners operating large clusters built in coordination with NVIDIA's tested reference architecture
  • ✓Access to cutting-edge NVIDIA GPUs including GB300 NVL72 and GB200 NVL72 in addition to H100 and H200
  • ✓Verified customer cost savings — CentML reported 5x lower inference costs compared to other major providers
  • ✓EU-based compute capacity (data center outside Helsinki) supports data-residency and regulatory compliance requirements
  • ✓24/7 solution architect assistance for multi-node cases is included at no additional charge
  • ✓Operates ISEG, the #19 most powerful supercomputer in the world, giving credible evidence of large-cluster capability

✗ Cons

  • ✗Pricing is not fully transparent on the homepage — custom quotes require contacting sales for enterprise configurations
  • ✗Smaller global footprint than AWS, GCP, or Azure — limited regional options outside Europe may affect latency-sensitive workloads
  • ✗Focused specifically on AI/ML compute rather than being a general-purpose cloud (no broad PaaS, serverless, or consumer-web services)
  • ✗Advanced features like InfiniBand clusters and managed Slurm target experienced ML engineers rather than beginners
  • ✗Smaller third-party ecosystem and marketplace compared to hyperscaler competitors

Frequently Asked Questions

Which NVIDIA GPUs does Nebius AI Cloud offer?+

Nebius provides the latest NVIDIA accelerators including GB300 NVL72, GB200 NVL72, B300, B200, H200, and H100 Tensor Core GPUs. Clusters are interconnected with NVIDIA InfiniBand and Quantum-X800 InfiniBand for low-latency multi-node training. You can scale from a single GPU up to pre-optimized clusters with thousands of GPUs. Drivers, CUDA, and networking come pre-configured so teams can start training or inference without manual hardware setup.

How does Nebius compare to AWS, GCP, and Azure for AI workloads?+

Compared to the hyperscalers, Nebius is purpose-built for AI rather than being a general cloud, which translates into meaningful cost and performance advantages — CentML reported 5x lower costs than other major providers after moving to Nebius. Nebius also holds Reference Platform NVIDIA Cloud Partner status, meaning its clusters are built in coordination with NVIDIA's tested reference architecture. The tradeoff is a smaller service catalog and fewer global regions. For pure GPU training and inference, it is highly competitive; for mixed workloads needing hundreds of managed services, hyperscalers may still fit better.

What orchestration and MLOps tools does Nebius support?+

Nebius offers Managed Kubernetes and Slurm-based cluster orchestration out of the box, along with fully managed MLflow, PostgreSQL, and Apache Spark services. You can manage infrastructure as code using Terraform, the Nebius API, or CLI, and there is also a web console for interactive management. Pre-built Terraform recipes and tutorials accelerate common setups. The platform integrates cleanly with frameworks like PyTorch, Kubeflow, and NCCL — Recraft used this combination to train a 20B-parameter generative design model.

Is Nebius AI Cloud suitable for EU compliance requirements?+

Yes. Nebius operates a data center 60 kilometers from Helsinki, Finland, providing EU-based compute capacity that helps customers meet data residency and regulatory requirements. CentML specifically cited enhanced compliance with EU compute requirements as a reason for choosing Nebius. Nebius also maintains a trust center documenting its security and compliance posture. For organizations regulated under EU data-protection rules or those preferring sovereign compute, this is a meaningful differentiator.

What support does Nebius provide for large multi-node training jobs?+

Nebius includes 24/7 expert support and dedicated assistance from solution architects for multi-node cases at no extra charge. The architect team has hands-on experience deploying thousands of GPUs — they helped Recraft overcome hardware configuration challenges when training their 20B-parameter foundation model, and supported vLLM in running large-scale inference experiments on DeepSeek R1 with zero hardware-related issues reported. An in-house AI R&D team also dogfoods the platform, meaning the infrastructure is continuously tuned against real ML workloads rather than theoretical benchmarks.
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What's New in 2026

Nebius elevated its NVIDIA Partner Network status to Reference Platform Cloud Partner, a tier reserved for select partners operating large clusters built in coordination with NVIDIA's tested reference architecture. The GPU catalog now includes the latest NVIDIA GB300 NVL72, GB200 NVL72, B300, and B200 accelerators alongside H200 and H100. Nebius also launched the AI Discovery Award 2026, an annual program offering $100,000 in cloud credits to startups working on AI for drug discovery, biotechnology, genomics, and HealthTech, with applications open until April 30, 2026. A Nebius Token Factory offering has also been added as a managed inference endpoint complement to the core AI Cloud.

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Website

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