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About
Nebius AI Cloud is an AI-first cloud platform designed to democratize access to cutting-edge GPU infrastructure for researchers, startups, and enterprises alike. Built on top-of-the-line NVIDIA hardware—including GB300 NVL72, GB200 NVL72, B300, B200, H200, and H100 GPUs—and backed by high-speed NVIDIA InfiniBand networking, Nebius offers the raw performance needed for the most demanding AI training and inference workloads. The platform supports flexible scaling from a single GPU all the way to pre-optimized multi-node clusters, orchestrated via Managed Kubernetes or Slurm. Nebius also provides fully managed services for popular MLOps and data tools like MLflow, PostgreSQL, and Apache Spark, reducing operational overhead for engineering teams. Developers and DevOps professionals benefit from a cloud-native experience with Terraform support, a REST API, CLI tooling, and an intuitive web console. Ready-to-go solution templates and detailed tutorials accelerate onboarding, while dedicated solution architects and 24/7 support cover multi-node and production deployments at no extra charge. Nebius operates its own AI-optimized, sustainable data centers—including a Helsinki supercluster ranked among the world's top 20 most powerful supercomputers. The platform is trusted by leading AI projects such as vLLM and CRISPR-GPT, making it a strong choice for teams that need reliable, cost-efficient, high-throughput GPU infrastructure for LLM training, fine-tuning, and inference at scale.
Key Features
- Latest NVIDIA GPU Fleet: Access cutting-edge NVIDIA GPUs including GB300 NVL72, GB200 NVL72, B300, B200, H200, and H100, backed by high-speed InfiniBand networking for demanding AI workloads.
- Scalable Cluster Orchestration: Scale seamlessly from a single GPU to thousands-strong superclusters using Managed Kubernetes or Slurm orchestration with fast distributed storage.
- Fully Managed AI/ML Services: Deploy MLflow, PostgreSQL, and Apache Spark with zero maintenance overhead, enabling teams to focus on model development rather than infrastructure management.
- Cloud-Native Developer Tooling: Manage infrastructure as code via Terraform, REST API, and CLI, or use the intuitive web console with ready-to-go solution templates and tutorials.
- 24/7 Expert Support: Receive around-the-clock support plus dedicated solution architect assistance for multi-node deployments, included at no additional cost.
Use Cases
- Training large language models (LLMs) and foundation models on multi-node GPU superclusters with high-speed InfiniBand interconnects.
- Fine-tuning open-source models like LLaMA or Mistral on proprietary datasets using managed compute infrastructure.
- Running high-throughput LLM inference in production using frameworks like vLLM on scalable GPU clusters.
- Accelerating AI research in life sciences, genomics, and drug discovery requiring heavy computational workloads.
- Building and deploying end-to-end MLOps pipelines using integrated managed services for MLflow, databases, and distributed computing.
Pros
- Top-Tier GPU Hardware: Access to the very latest NVIDIA GPU generations (Blackwell, Hopper) and InfiniBand networking ensures competitive performance for large-scale AI training and inference.
- Flexible Scaling: Supports workloads from solo GPU experimentation to multi-thousand GPU superclusters, making it suitable for both early-stage research and production deployments.
- Comprehensive Managed Services: Built-in managed services for MLflow, databases, and orchestration reduce DevOps burden and accelerate time-to-model for AI teams.
- Dedicated Expert Support: Free 24/7 expert support and solution architect guidance for multi-node cases differentiates Nebius from hyperscaler commodity offerings.
Cons
- Enterprise-Focused Pricing: As a paid, contact-sales platform targeting serious AI workloads, costs may be prohibitive for individual hobbyists or very early-stage projects with minimal budgets.
- Limited Regional Presence: Data centers are currently concentrated in specific regions (e.g., Helsinki), which may introduce latency concerns for users in other geographies.
- Steep Learning Curve for Cluster Orchestration: While managed services ease operations, configuring and optimizing large multi-node GPU clusters still requires significant infrastructure expertise.
Frequently Asked Questions
What GPU types does Nebius AI Cloud offer?
Nebius provides access to the latest NVIDIA GPU generations including GB300 NVL72, GB200 NVL72, B300, B200, H200, and H100, all connected via NVIDIA InfiniBand and Quantum-X800 networking for maximum throughput.
Can I scale from a small experiment to a large training cluster?
Yes. Nebius supports flexible scaling from a single GPU instance up to pre-optimized clusters with thousands of GPUs, orchestrated via Managed Kubernetes or Slurm, making it suitable for any stage of AI development.
What managed services are available on Nebius?
Nebius offers fully managed deployments of MLflow for experiment tracking, PostgreSQL for relational data, and Apache Spark for large-scale data processing—all maintained with zero operational overhead.
How does Nebius handle infrastructure as code?
Nebius supports Terraform for infrastructure provisioning, along with a REST API and CLI for programmatic control. A web console is also available for teams that prefer a GUI-based approach.
What support is included with Nebius AI Cloud?
All Nebius customers receive 24/7 expert technical support. For multi-node and large-scale deployments, dedicated solution architects are available at no additional charge to help design and optimize infrastructure.
