Rafay-Powered Jupyter Notebooks as a Service (JNBaaS)

Rafay-powered Jupyter Notebooks as a Service (JNBaaS) allows providers and enterprises to offer governed, on-demand JupyterLab environments for data science, AI, and ML teams. Developers and researchers often face delays in provisioning GPU-backed environments or managing dependencies.

Rafay eliminates these challenges by offering instant, fully managed Jupyter notebooks equipped with pre-installed AI/ML libraries, GPU access, and built-in collaboration tools.

Service providers, enterprises, and sovereign cloud operators can deliver ready-to-use, GPU-enabled notebook workspaces that accelerate experimentation and improve productivity.

• Instant Access: Launch JupyterLab notebooks immediately with no setup required ‍
• Seamless GPU Integration: Run notebooks directly on GPUs for model training, testing, and inference ‍
• AI/ML-Ready Environments: Pre-configured libraries and persistent storage simplify data preparation and prototyping

Simplify and Automate AI/ML Experimentation

Rafay automates how teams access, share, and manage GPU-powered notebook environments—removing operational friction from model development.

Pre-Configured Environments

PyTorch, TensorFlow, CUDA, and other frameworks are ready to use out of the box.

Persistent Storage

Maintain consistent access to datasets and results for reproducible workflows.

Collaboration Controls

Enable secure notebook sharing with role-based access and team visibility.

Integrated Governance

Ensure compliance, traceability, and consistent policies across all environments.

Accelerate Experimentation and Improve Resource Utilization

Move from concept to GPU-backed experimentation in seconds, accelerating AI/ML projects.

Monetize notebooks as value-added SKUs to increase utilization per GPU.

Enable secure, multi-user access with reproducibility and version control.

Deliver compliant, notebook services aligned with data residency and governance requirements

Featured Resources

• Operationalizing AI Fabrics with Aviz ONES, NVIDIA Spectrum-X, and Rafay Discover the new AI operations model available to enterprises that enables self-service consumption and cloud-native orchestration for developers. Learn More

• The Definitive GPU PaaS Reference Architecture Understand what it takes to deliver the right GPU infrastructure to your business. Learn More

• Unlock Your AI Potential with Cisco and Rafay: Transform AI PODs into a Self-Service GPU Cloud Cisco provides AI-optimized infrastructure. Rafay makes it usable across teams, tenants, and use cases in days. Learn More

• The CIO’s guide to scalable, compliant, and developer-ready AI deployment Orchestrating the future of AI: The CIO’s guide to scalable, compliant, and developer-ready AI deployment Learn More

• Rafay Named Outperformer in 2025 GigaOm Radar Report for Managed Kubernetes The latest Radar report from GigaOm, Managed Kubernetes Rafay is ranked as an “Outperformer” for its solution. Learn More

• Building AI Value within Borders Rafay's central orchestration platform facilitates efficient, self-service infrastructure and AI application management. Learn More

• GPU cloud evaluation report Evaluating how the Rafay Platform delivers a GPU cloud for enterprises and cloud service providers by PivotNine. Learn More

• How Enterprise Platform Teams Can Accelerate AI/ML Initiatives This paper explores the key challenges that organizations experience supporting these initiatives, as well as best practices for successfully leveraging Kubernetes to accelerate AI/ML projects. Learn More

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