Nebius AI Cloud Pros & Cons: What Nobody Tells You [2026]

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.

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.

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.

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.

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.