Jupyter provides interactive computing environments for data science, AI, and ML. This product offers two deployment modes: JupyterLab for single-user notebook workflows with PyTorch and CUDA support, and JupyterHub for multi-user collaborative environments on Kubernetes. Both include GPU acceleration, making them ideal for training, fine-tuning, and experimentation without manual infrastructure setup.
Develop and iterate in notebooks with code, outputs, and visualization in one place.
Use CUDA-enabled configurations for accelerated AI and ML workloads.
Deploy JupyterHub on Kubernetes for teams with isolated notebook environments.
Run training and experimentation workflows with a preconfigured deep learning stack.
Additional Nebius infrastructure costs may apply. Use the Nebius Pricing Page to estimate your infrastructure costs.
Root access & custom setup. Maximum performance tuning. Direct hardware control.
Multi-user notebooks on Kubernetes. Scalable, collaborative environment for teams.
Reliable AI infrastructure backed by top-tier NVIDIA GPUs, purpose-built for demanding inference workloads. Multiple deployment methods — virtual machines for full hardware control, Kubernetes for scalable cluster deployments, and managed serverless applications for teams that want inference running without infrastructure overhead
Learn about Nebius AI CloudNebius meets a broad set of security and compliance standards. Fine-grained IAM controls, audit logs, and encrypted storage are available out of the box — so teams can meet security requirements without additional tooling.
Explore the Trust centerApplication support
Provided by Project Jupyter. See the documentation and project links above.
Infrastructure support
Provided by Nebius for the underlying cloud infrastructure.
