Nebius provides a unified AI cloud platform that integrates data storage, GPU‑accelerated compute, managed Kubernetes, and end‑to‑end model lifecycle tools such as MLflow, serverless inference endpoints, and observability services. By offering pre‑configured virtual machines, InfiniBand GPU clusters, and turnkey applications, it lets AI developers and data‑science teams build, train, and deploy large‑scale models without managing underlying infrastructure.
Funding
$1B raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Building, training, and deploying AI models at scale requires coordinating multiple specialized services—data storage, compute resources, model management, and inference infrastructure—often across disparate cloud providers, leading to high operational overhead and limited performance consistency.
Solution
Nebius offers a unified AI cloud platform that integrates end‑to‑end services for the entire AI lifecycle, from data ingestion and labeling to model training, fine‑tuning, and production inference. The platform provides GPU‑accelerated virtual machines, InfiniBand‑connected GPU clusters, and managed Kubernetes environments to run large‑scale workloads efficiently. Built‑in tools such as MLflow clusters, serverless AI endpoints, and a container registry simplify experiment tracking, model versioning, and deployment. Nebius also includes data preparation services, object storage compatible with S3, and observability components for metrics, logs, and tracing, enabling developers to monitor and optimize AI pipelines without managing underlying infrastructure. By delivering these capabilities through a single, globally available cloud, Nebius reduces the time and expertise needed to move AI projects from prototype to production.
Target Audience
Primary customers are AI developers, data science teams, and enterprise engineering groups that need a cohesive, high‑performance cloud environment to build, train, and deploy machine‑learning models at scale.
Features
- GPU‑enabled virtual machines, InfiniBand GPU clusters, and Slurm‑managed Soperator clusters for high‑performance training and distributed computing
- Managed Kubernetes clusters with native GPU support for containerized AI workloads
- Integrated storage solutions: S3‑compatible object buckets, block/file storage, and PostgreSQL® clusters for datasets and model artifacts
- Serverless AI services offering scalable endpoints and job execution for real‑time inference
- MLflow clusters for experiment tracking, model registry, and reproducible training pipelines
- Turnkey applications such as JupyterLab® and NVIDIA NIM to accelerate development
- Comprehensive observability stack with metrics dashboards, log aggregation, and distributed tracing
- Terraform provider and CLI tools for infrastructure‑as‑code provisioning and automated resource management