Nebius AI is an AI-native cloud platform headquartered in Amsterdam, founded in 2023. The published catalogue is HGX H200, HGX H100, HGX B200, L40S, and RTX PRO 6000. GB200 rows exist only as drafts, so this page does not treat them as available.
Why Nebius AI stands out
The tagline in our data is an AI-native cloud for ML teams. Ease of use in our scoring is at the top of the scale. Billing is per second. Our pricing-competitiveness score sits below the middle of the directory, so do not read this as a bargain cloud. Compare the rows.
The Kubernetes support and Docker compatibility mean you can bring your existing MLOps stack without rearchitecting anything. Multi-GPU configurations are supported natively, and persistent storage means your datasets and checkpoints survive instance restarts.
Being Europe-based is also increasingly relevant for teams with data residency requirements or who simply want lower latency to EU regions.
Pros
- Excellent ease of use — one of the cleanest onboarding experiences in the GPU cloud space
- Per-second billing, with credit card and bank transfer recorded
- Published HGX H200, HGX H100, and HGX B200 rows
- Kubernetes, Docker, Jupyter, an API, multi-GPU, and persistent storage are all set
- Multi-GPU support for large training runs
- Persistent storage included
- Spot instances available for cost-conscious workloads
- Marked SOC 2 compliant
Cons
- Founded in 2023 — less track record than established players like Vast.ai or Lambda Labs
- No reserved instances, so long-term cost predictability is limited
- GPU variety is narrower than broader marketplaces
Getting started
- Visit Nebius AI and create an account — credit card or bank transfer accepted
- Browse the GPU catalog and select your instance type (H100 SXM is a solid default for most training workloads)
- Choose between on-demand or spot instances depending on your fault tolerance
- Deploy through the API or Kubernetes. Both are recorded. Spot is listed. Reserved instances are not.
- Connect persistent storage to your instance before loading datasets
Best for: ML engineers and research teams who want per-second billing, H200, H100, and B200 hardware, Kubernetes, and a provider our data marks SOC 2 compliant.