Here’s the provider profile for Nebius AI:
Nebius AI
Nebius AI is an AI-native cloud platform headquartered in Amsterdam, founded in 2023 as a spinoff from Yandex’s international cloud infrastructure. Despite being relatively young, Nebius has moved fast: they’ve built a serious GPU cloud with a hardware lineup that goes straight to the top of the stack, including NVIDIA H200s, H100s, and Blackwell-generation B200s. If you’re building ML infrastructure and want access to cutting-edge silicon without dealing with hyperscaler complexity, Nebius is worth a hard look.
Why Nebius AI stands out
Nebius isn’t trying to be AWS. It’s purpose-built for ML teams, and that focus shows in the product experience. The platform earned a perfect 10 for ease of use — it genuinely feels designed by people who run training workloads, not general-purpose cloud architects. The GPU selection prioritizes the hardware that actually matters for modern LLM training and inference, and the per-second billing means you’re not hemorrhaging money during setup and teardown.
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 competitive H200 and H100 pricing
- Access to NVIDIA Blackwell (B200/GB200) — rare among independent clouds
- Kubernetes-native with full Docker support
- Multi-GPU support for large training runs
- Persistent storage included
- Spot instances available for cost-conscious workloads
Cons
- Founded in 2023 — less track record than established players like Vast.ai or Lambda Labs
- Not SOC 2 compliant yet — may block adoption at larger enterprises
- No reserved instances, so long-term cost predictability is limited
- GPU variety is narrower than broader marketplaces
- Enterprise readiness is still maturing (rated 5/10)
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 via their web console, Kubernetes integration, or API — all three are well-supported
- Connect persistent storage to your instance before loading datasets
Best for: ML engineers and research teams who want a polished, AI-first cloud experience with access to top-tier NVIDIA hardware (H200, H100, Blackwell), and don’t need the compliance certifications of an enterprise hyperscaler.