Nebius review: Quick verdict
This Nebius review focuses on an enterprise-grade cloud provider dedicated exclusively to machine learning infrastructure. Nebius offers high-end NVIDIA compute, specifically H100, H200, and Blackwell GPUs, backed by InfiniBand networking and strict European data residency compliance. Its 1-second billing increments and preemptible instances provide precise cost control for large-scale training jobs. Teams looking for cheap consumer-grade cards like the RTX 4090 or single-click hobbyist deployments will find the platform over-engineered for their needs. If you need massive scale and GDPR-compliant data centers, Nebius is built for that scale.
Key takeaways
- Nebius specializes in HGX clusters and high-bandwidth InfiniBand networking for distributed multi-node training workflows.[source]
- Compute billing operates on a 1-second granularity, meaning you never pay for an unused fraction of an hour after spinning down an instance.[source]
- High-end instances bundle the GPU, host vCPU, and RAM into a single unified hourly rate, simplifying cost estimation.[source]
- Data centers are located in Europe, providing a straightforward compliance path for companies navigating GDPR and local data sovereignty laws.
- The platform requires technical proficiency with cloud architecture, as it is not a drag-and-drop Jupyter environment for beginners.
How the service works
Nebius AI Cloud operates a managed infrastructure model built from the ground up for AI workloads. Unlike generalist hyperscalers like AWS or Azure that run databases, web servers, and GPUs on the same network fabric, Nebius designs its datacenters specifically for tensor operations and large-scale model training.
The core of the platform relies on NVIDIA's enterprise architectures. They deploy HGX baseboards connected via high-speed InfiniBand to prevent network bottlenecks during distributed training.[source] When you rent instances on Nebius, you interact with a standard cloud console or API to provision virtual machines or deploy containers via their Managed Kubernetes service.
Billing is highly granular. The system tracks resource usage down to the second. When you stop a virtual machine, the compute charges stop immediately, though you continue to pay the storage rate for the attached disk volumes.[source] This setup appeals directly to ML operations teams running massive batch jobs. They can spin up a hundred H100 instances, process a dataset in forty-seven minutes, spin them down, and pay exactly for forty-seven minutes of time.[source]
Pricing
Nebius prices its hardware competitively against other specialized neoclouds, though exact rates fluctuate based on regional demand and hardware updates.
For on-demand, pay-as-you-go workloads, an H100 SXM instance costs approximately $3.85 to $3.98 per hour.[source] This undercuts CoreWeave's on-demand H100 rate of $6.16 per hour and sits roughly in line with Lambda Labs' rate of $3.99 per hour.[source] [source]
The H200 SXM, offering larger memory capacity and bandwidth, lists at roughly $4.50 to $4.63 per hour on demand.[source]
For fault-tolerant workloads like batch inference or checkpoints-enabled training, Nebius offers preemptible virtual machines. These instances can be interrupted by the provider when capacity is needed elsewhere. The preemptible rate for an H100 drops to around $2.15 per hour, and an H200 drops to $2.45 per hour.[source]
If your organization plans to run workloads continuously, Nebius provides long-term commitment discounts. Committing to a specific capacity for several months can reduce the on-demand rate by up to 35 percent.[source]
Unlike some providers that charge separately for the GPU, the host vCPUs, and the system RAM, Nebius uses a unified billing model for its high-end instances. The listed rate for an H100 or H200 includes the necessary CPU and memory allocations, making the monthly invoice easier to predict.[source]
| Provider | On-demand $/hr | Spot $/hr | Availability |
|---|---|---|---|
| TensorDock Cheapest | $2.25 | n/a | High |
| Lambda | $3.29 | n/a | Medium |
| Lambda | $3.99 | n/a | Medium |
| CoreWeave | $6.16 | $2.46 | High |
Important features
Nebius goes beyond renting raw GPUs by providing a software ecosystem designed to support large engineering teams.
1-second billing granularity
Most cloud providers charge by the minute or the hour. Nebius calculates compute usage by the second. If your training script finishes in 14 minutes and 32 seconds, your bill reflects exactly that duration.[source] At the scale of a 64-GPU cluster, rounding up to the nearest hour wastes hundreds of dollars per run.[source]
InfiniBand networking
Training large language models across multiple nodes requires moving massive amounts of gradient data between servers. Standard ethernet connections throttle this process, leaving expensive GPUs idle while they wait for network packets. Nebius connects its HGX clusters with NDR InfiniBand, enabling the non-blocking bandwidth necessary for efficient distributed training.[source]
Managed Kubernetes and object storage
Instead of forcing teams to configure their own orchestration layers, Nebius integrates a Managed Service for Kubernetes.[source] Data scientists can deploy containerized workloads across a fleet of GPUs using standard commands. They also provide native, S3-compatible object storage designed to handle the high throughput demands of loading training datasets into GPU memory.[source]
European data residency
Companies operating in the European Union face strict data sovereignty requirements. Nebius locates its core infrastructure in Europe, allowing organizations to train models on proprietary or sensitive data without transferring that information to servers physically located in the United States.[source]
Who should use it
Enterprise machine learning teams and funded AI startups are the primary audience for Nebius. If you need to reserve a cluster of 64 or 128 H100s to pre-train a foundation model, the platform provides compute hardware, InfiniBand networking, and API tooling to execute the job efficiently.[source]
European companies dealing with GDPR compliance or strict data localization policies will find Nebius a strong alternative to US-centric hyperscalers. The location of the physical datacenters addresses data residency requirements without additional engineering work.
Finally, organizations with heavy batch-processing workloads can use the 1-second billing and preemptible instances to cut their monthly infrastructure spend significantly. You can read more about planning these workloads in our guide on how to choose a GPU cloud provider.
Who should skip it
Solo developers and hobbyists should look elsewhere. Nebius does not cater to the low-end market. You will not find cheap consumer RTX 4090s or shared gaming cards on this platform.
Teams looking for a fully managed, instant-launch Jupyter notebook environment without dealing with virtual machine configuration or Kubernetes manifests will also find the platform frustrating. Nebius provides infrastructure, not an abstracted developer playground. Those teams should consult our list of the best GPU cloud for beginners.
Alternatives to Nebius AI Cloud
If Nebius does not fit your operational model, several other specialized providers cover different segments of the market.
CoreWeave provides a similar enterprise-focused experience if you need large-scale compute but prefer infrastructure located in the United States. They offer bare-metal Kubernetes deployments and maintain a large inventory of NVIDIA hardware, though their on-demand rates for H100s are generally higher than Nebius. Read the CoreWeave review for more details.
Lambda Labs offers a clean interface and competitive pricing on H100 and A100 instances for teams that want straightforward, on-demand GPU virtual machines without the complexity of managed Kubernetes. Read the Lambda Labs review to compare their instance model.
RunPod covers the lower and middle tiers of the market if your budget requires consumer-grade hardware or you want to deploy a single container with a few clicks. They offer RTX 3090s and 4090s alongside enterprise cards, with a simpler serverless deployment model. Check the RunPod review for pricing.
Pros and cons
Pros
- Highly competitive pricing on enterprise H100 and H200 instances.[source]
- 1-second billing granularity eliminates paying for idle time after a job finishes.[source]
- InfiniBand networking prevents data bottlenecks during distributed training.[source]
- European datacenters simplify data residency compliance for EU companies.
- Deeply integrated Managed Kubernetes service for container orchestration.
Cons
- No consumer-grade GPUs available for low-budget prototyping.
- Requires cloud engineering expertise to deploy and manage workloads effectively.
- Lacks the simple, one-click interactive notebook environments found on developer-focused platforms.
Frequently asked questions
Does Nebius offer RTX 4090 or RTX 3090 instances?
No. Nebius focuses entirely on enterprise-grade NVIDIA hardware like the H100, H200, and Blackwell architectures. They do not supply consumer gaming cards for compute workloads.
How does Nebius billing work?
Nebius uses a pay-as-you-go model with a 1-second billing unit. You are charged a unified rate for the GPU, CPU, and RAM while the virtual machine is running. When you stop the machine, compute charges cease, and you only pay for the stored data.[source]
Where are the Nebius datacenters located?
Nebius operates its primary infrastructure out of Europe, with major facilities located in Iceland and Finland.[source] This allows them to offer strict data residency guarantees for European organizations.
Can I get a discount on Nebius?
Yes. Nebius offers preemptible instances for fault-tolerant workloads at a significantly reduced hourly rate. They also provide commitment discounts of up to 35 percent if you reserve cluster capacity for a set number of months.[source]
What is the difference between Nebius and AWS EC2?
Nebius is a specialized AI cloud. Their network architecture, storage, and orchestration layers are built specifically to support high-throughput tensor operations. AWS EC2 is a general-purpose cloud that runs everything from web servers to databases, often charging a steep premium for access to their top-tier GPU instances and network egress.
Methodology and sources
We evaluate cloud providers by reviewing their official pricing documentation, architecture specifications, and service agreements. We do not run proprietary benchmarks, latency tests, or hands-on trials. We rely on the vendor's technical documentation to map their infrastructure capabilities to specific machine learning use cases. Read more on our methodology page.