10 Best Nebius Alternatives in 2026: Cheaper H100/H200 Without the Wait

Nebius is a solid GPU cloud, especially for European teams needing data residency compliance. But it has limits: a narrow GPU catalog, quota-based access, and geographic focus that doesn't work for everyone. Here are 10 alternatives worth evaluating in 2026.

Why Teams Look for Nebius Alternatives

Nebius has built real GPU infrastructure with competitive hardware and GDPR-friendly EU data centers. But four friction points keep coming up:

Quota process. Accessing H100 and H200 GPUs at scale requires going through an approval workflow. Teams that need compute quickly (for a deadline, an experiment, or a sudden training push) get blocked waiting for quota allocation.

EU-centric infrastructure. Nebius's infrastructure is concentrated in Europe, with data centers in Finland, UK, Iceland, and France. The platform has expanded to the US (Kansas City and New Jersey), but teams in North America still face higher latency with no data residency benefit for the premium they are paying. Global coverage remains limited compared to providers with truly multi-region footprints.

Narrow GPU catalog. Nebius offers HGX H100 ($2.95/hr), HGX H200 ($3.50/hr), HGX B200 ($5.50/hr), L40S with Intel CPUs (from $1.55/hr), and L40S with AMD CPUs (from $1.82/hr). The NVIDIA GB200 NVL72 and GB300 NVL72 Blackwell cluster configs are on pre-order (contact Nebius for access). There is no A100, no RTX 5090, no GH200, no consumer GPUs. If your workload needs hardware outside that list, you have to look elsewhere.

Pricing. Nebius HGX H100 runs $2.95/hr on-demand per GPU (with 16 vCPUs and 200 GB RAM included). That is meaningfully higher than newer marketplace-model providers offering the same hardware for less. Nebius does offer discounted rates of $2.00/hr for H100 with a commitment of hundreds of units, but that requires a sales process and multi-month reservation.

1. Spheron: Best Overall Nebius Alternative

H100 PCIe: $2.01/hr on-demand | H200 SXM5: $4.23/hr on-demand / $1.43/hr spot | RTX 5090: $0.76/hr | A100 80G: $0.61/hr on-demand / $0.45/hr spot

Prices fluctuate over time. Rates shown are based on available offers as of 10 March 2026.

Spheron is the strongest all-around alternative to Nebius for most teams. Rather than running its own data centers, Spheron aggregates bare-metal GPU capacity from a global network of vetted data center partners, which solves both the pricing and the access problems that Nebius creates.

The pricing gap is significant. An H100 PCIe on Spheron runs $2.01/hr on-demand versus Nebius's $2.95/hr on-demand rate for HGX H100. For an 8x H100 training job running 30 days:

• Spheron: $2.01 × 8 × 720 = $11,578/month
• Nebius: $2.95 × 8 × 720 = $16,992/month

That is over $5,414 per month in savings, more than $64,973 per year, for the same hardware running the same workload.

Where Nebius restricts access behind a quota process, Spheron lets you deploy immediately after signup. Where Nebius's catalog covers H100, H200, L40S, and HGX B200, Spheron offers 30+ GPU SKUs including RTX 5090, GH200, A100, L40S, and more. Where Nebius is primarily EU-based, Spheron's partner network spans global regions with no latency penalty for teams outside Europe.

Spheron also provides full bare-metal root SSH access with no container restrictions, so you can install custom CUDA drivers, modify kernel settings, or run any software stack you need. Billing is per-minute rather than per-hour, which matters when running multiple shorter training jobs. There are no contracts or minimum commitments.

For teams that need more hardware options, instant access, or global coverage, Spheron is the strongest Nebius alternative. Compare GPU pricing →

2. RunPod

H100 from ~$1.99/hr (community cloud) | Per-second billing

RunPod is an AI-focused GPU cloud with two tiers: Community Cloud (cheaper, independent hosts) and Secure Cloud (managed infrastructure). The platform is well-known for its broad GPU selection and developer-friendly experience.

Pros: Wide GPU selection including RTX 5090, H200, B200, and consumer RTX cards. Per-second billing is the most granular available. Pre-built templates for PyTorch, stable diffusion, LLM fine-tuning, and more make setup fast. Serverless GPU endpoints add flexible inference options for teams that need pay-per-request scaling.

Cons: Community Cloud instances run on individual hosts who can take systems offline mid-job, introducing availability risk for critical workloads. Workloads run in containers with no bare-metal access or custom driver installation. Support quality varies between community and secure tiers.

Best for: Teams that want a simple interface, a large template library, and pay-per-second billing. Developers exploring diverse GPU hardware or running varied workloads who want fast onboarding and a large community ecosystem.

For a detailed comparison, see our RunPod alternatives guide.

3. Lambda Labs

H100 PCIe: $2.49/hr | A100: $1.29/hr | Free egress

Lambda has been in the GPU cloud business since 2019 and has earned strong credibility with research labs and AI companies. The platform is reliable, well-maintained, and deep NVIDIA partnerships keep hardware supply relatively stable.

Pros: No egress fees, a genuine advantage for teams that move large datasets between training runs and storage. Strong support for large-scale distributed training up to 512 GPUs with InfiniBand. Lambda Stack pre-installs PyTorch, TensorFlow, and CUDA, reducing setup time. Responsive support team with dedicated account managers for larger accounts.

Cons: H100 on-demand inventory goes out of stock regularly during peak demand. Best pricing requires a 3-year reserved commitment. Per-hour billing rounds up partial hours. More expensive than Spheron and Hyperstack for on-demand access. No consumer GPU options.

Best for: Academic research labs and well-funded AI teams that value reliability and institutional credibility, are willing to commit capacity in advance, and frequently move large datasets where free egress saves meaningful cost.

See our Lambda Labs alternatives guide for a full comparison.

4. Vast.ai

H100 from ~$1.14/hr (marketplace) | Marketplace pricing

Vast.ai is a decentralized GPU marketplace where independent hosts list hardware and renters choose based on price, location, and host rating. The marketplace model creates the lowest possible floor prices; community hosts can list H100 PCIe from around $1.14/hr, while verified datacenter hosts typically command $1.87/hr or more depending on current supply.

Pros: Lowest price ceiling available when marketplace supply is strong. Extremely broad hardware selection, everything from RTX 3090 consumer cards through H100 and H200. Flexible bidding lets cost-conscious teams optimize against current supply. A verified datacenter tier offers more reliable hosts at a modest premium.

Cons: No uptime SLAs; unverified hosts can go offline mid-job. Hardware quality and networking performance vary significantly between hosts. Storage fees accrue even when instances are paused. Standard GPU instances run as Docker containers; bare-metal or custom driver installation is not available on the standard tier. Security and trust model differs significantly from managed clouds.

Best for: Cost-conscious researchers and developers running interruptible batch workloads who prioritize price above all else and are comfortable managing infrastructure variability.

5. Hyperstack

H100 PCIe: $1.90/hr | H100 SXM: $2.40/hr | H200 SXM: $3.50/hr | A100: $1.35/hr | L40: $1.00/hr

Hyperstack is NexGen Cloud's GPU cloud platform, built with a focus on European data residency and GDPR compliance. For EU teams looking for a Nebius alternative that keeps data in Europe, Hyperstack is worth evaluating. At $1.90/hr for H100 PCIe on-demand, it is significantly cheaper than Nebius's $2.95/hr on-demand HGX H100 rate. Hyperstack has also expanded its catalog to include H200 SXM ($3.50/hr), HGX B200, HGX B300, and RTX Pro 6000 configurations.

Pros: GDPR-compliant infrastructure in Europe and North America, addressing the same compliance need as Nebius at lower cost. VM hibernation for pausing workloads without deleting environments. InfiniBand available for H100 NVLink cluster configurations. Expanded GPU catalog now covers H100, H200, B200, B300, A100, and L40 across on-demand and reservation tiers. Competitive on-demand pricing without requiring a sales process.

Cons: Smaller company with less market recognition than Lambda or CoreWeave. Documentation and support ecosystem less mature than established providers. Enterprise-scale deployments require direct contact for custom contracts. H200 SXM on-demand ($3.50/hr) matches Nebius's H200 price, so the cost advantage over Nebius is stronger for H100 PCIe workloads than for H200.

Best for: EU teams that need data residency or GDPR compliance but want an alternative to Nebius, particularly if Nebius's quota process or pricing is a concern. A strong second choice when EU data residency is a hard requirement.

6. CoreWeave

HGX H100 8-GPU node: $49.24/hr (~$6.15/hr per GPU on-demand) | HGX H200: $50.44/hr | B200: $68.80/hr | Contracts required for competitive rates

CoreWeave is the enterprise GPU cloud: Kubernetes-native infrastructure, InfiniBand networking across nodes, and large cluster support up to 256+ GPUs. NVIDIA-backed with priority access to new hardware. If you are training a frontier model at scale, CoreWeave can provision the infrastructure. But the enterprise positioning comes with enterprise friction.

Pros: Best-in-class InfiniBand networking for large-scale distributed training. Kubernetes-native orchestration for complex ML pipelines. Large cluster availability. Enterprise SLAs with guaranteed uptime. Strong NVIDIA partnership providing early access to new hardware generations.

Cons: On-demand HGX H100 pricing runs $49.24/hr for an 8-GPU node (approximately $6.15/hr per GPU), making it one of the most expensive options on this list. HGX H200 is $50.44/hr per 8-GPU node; the B200 8-GPU node is $68.80/hr. New users typically go through an enterprise onboarding process (credit checks, account vetting) before gaining access. Competitive reserved pricing requires contract negotiations and multi-month commitments.

Best for: Large enterprises and frontier AI labs training models at massive scale (100+ GPUs) with multi-year compute budgets who need dedicated infrastructure and guaranteed capacity.

7. Paperspace (by DigitalOcean)

H100: $5.95/hr | A6000: $1.89/hr

Paperspace, now part of DigitalOcean, is best known for its Gradient Notebooks product, a Jupyter-like environment with GPU backing. The platform is optimized for data scientists who work primarily in notebooks and want to attach compute without managing infrastructure.

Pros: Best-in-class notebook experience through Gradient. Simple onboarding for individual data scientists and students. DigitalOcean ecosystem integration for storage and networking. Good for education and learning environments where ease of use matters more than cost optimization.

Cons: Highest H100 pricing on this list at $5.95/hr. A $39/month Growth subscription is required to access high-end GPUs. Limited GPU catalog with no H200 or B200. H100 inventory frequently unavailable.

Best for: Individual data scientists and students who primarily work in notebooks and need occasional GPU access for experimentation. Teams already embedded in the Gradient ecosystem who value integrated ML workflow tools over cost.

8. AWS (P-series / Trn instances)

H100 SXM: ~$3.90/hr per GPU | P5 (8x H100): ~$31.20/hr on-demand | P4d (8x A100 40GB): ~$18.40/hr on-demand | Trn1 (16x Trainium1): ~$21.50/hr

AWS offers GPU instances through P-series (NVIDIA GPUs) and Trainium-based Trn instances. Current H100 capacity is available via P5 (p5.48xlarge) and P5e instances, while P4d instances provide A100 40GB access for teams with existing workloads. AWS cut P5 instance prices by 45% in June 2025, bringing on-demand H100 pricing to approximately $3.90/hr per GPU. For organizations deeply invested in the AWS ecosystem, the appeal is tight integration with S3, SageMaker, IAM, and compliance certifications that smaller GPU clouds cannot match.

Pros: Global coverage across 30+ regions. Deep integration with S3, SageMaker, VPC, and IAM for teams already running data pipelines on AWS. Full compliance certification catalog (SOC 2, HIPAA, FedRAMP) for regulated industries. Managed services reduce operational overhead for teams without dedicated MLOps staff.

Cons: Significantly more expensive than specialized GPU clouds for raw compute. Hidden egress and storage costs add meaningfully to total bills. Complex billing makes cost forecasting difficult. Governance and procurement processes add lead time for new instance types. For pure GPU compute, AWS is rarely competitive on price versus specialized providers.

Best for: Organizations already committed to AWS that need GPU compute tightly integrated with other AWS services, especially in regulated industries where AWS compliance certifications are required. For tips on managing costs, see our guide to avoiding unexpected AWS charges.

9. Latitude.sh

H100 SXM: ~$2.99/hr | A100 80GB: ~$2.49/hr | RTX 4090: ~$0.99/hr | Bare-metal, no hypervisor

Latitude.sh is a bare-metal cloud with GPU servers available on-demand and via reservation. Every instance runs directly on hardware with no virtualization layer, so you get the full performance of the server without hypervisor overhead. The platform targets developers and ML teams who want root access and raw compute without the management complexity of building their own data center contracts.

Pros: True bare-metal with full root SSH access, no hypervisor, no shared tenancy. Clean developer experience with a straightforward API and Terraform provider for infrastructure-as-code workflows. Global locations across North America, Europe, Latin America, and Asia-Pacific. Competitive pricing on A100 and RTX 4090 hardware. No egress fees. On-demand provisioning with deployments typically live within minutes.

Cons: GPU catalog is narrower than marketplace providers, primarily H100, A100, and RTX 4090. No container platform or managed ML tooling; you bring your own stack. H100 on-demand pricing (~$2.99/hr) is higher than Spheron's $2.01/hr. Smaller scale than hyperscalers means large multi-node cluster availability can be limited.

Best for: Developer and ML teams who want bare-metal performance with a clean API and no virtualization overhead, particularly for workloads that benefit from direct hardware access or require custom kernel configurations. A good fit for teams that find Nebius's lightweight virtualization model limiting but don't need the full complexity of CoreWeave.

10. Modal

H100 SXM: ~$4.27/hr | A100 80GB: ~$3.04/hr | A10G: ~$0.90/hr | Per-second billing | No infrastructure management

Modal is a serverless GPU compute platform built for Python developers. Rather than provisioning instances or managing SSH access, you annotate Python functions with GPU requirements and Modal handles orchestration, scaling, and teardown automatically. It has become popular with ML teams who want to run training jobs and inference endpoints without managing infrastructure.

Pros: Zero infrastructure management, no instance provisioning, no SSH, no cluster setup. Per-second billing with no idle charges; containers spin up in seconds and shut down automatically when work is done. Built-in support for persistent volumes, secrets management, scheduled jobs, and web endpoints. Strong developer experience with fast iteration loops. Works well for inference APIs where traffic is bursty and unpredictable.

Cons: GPU pricing is higher than raw compute alternatives: H100 SXM runs ~$4.27/hr versus $2.01/hr on Spheron. No bare-metal access or custom driver installation. Not designed for long-running training jobs where the per-second overhead adds up and raw cost per GPU-hour matters more than developer convenience. Vendor lock-in to Modal's Python SDK and container model. Cold starts add latency for latency-sensitive inference paths.

Best for: Python-native ML teams who want serverless GPU compute for inference endpoints, batch processing pipelines, and fine-tuning jobs without managing infrastructure. Particularly strong for teams that value fast iteration and don't want to think about instance types, autoscaling, or cluster configuration.

How to Choose the Right Nebius Alternative

Bottom Line

For most teams leaving Nebius or evaluating it, Spheron is the strongest alternative: broader hardware selection, instant access without a quota process, global coverage through a wide datacenter partner network, and H100 PCIe pricing at $2.01/hr on-demand versus Nebius's $2.95/hr on-demand rate. If EU data residency is a hard requirement, Hyperstack offers comparable GDPR compliance at a meaningfully lower price point. For everything else, match your priority (price, scale, or simplicity) with the provider table above.

Ready to move beyond Nebius? Spheron has H100, H200, RTX 5090, and 30+ other GPUs available right now, no quota, no waitlist.

Compare GPU pricing →