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Nebius and Saturn Cloud Launch First-in-Class AI MLOps Cloud with Support for NVIDIA AI Enterprise
Newsfileยท 2025-06-11 11:00
Core Insights - Nebius and Saturn Cloud have launched a first-in-class AI MLOps cloud solution that integrates NVIDIA Hopper GPUs and the NVIDIA AI Enterprise software stack, providing a comprehensive infrastructure for AI/ML engineers [1][6][10] Group 1: Product Offering - The new solution combines Nebius's AI cloud capabilities with Saturn Cloud's MLOps platform, allowing users to instantly access Jupyter notebooks and manage cloud resources on NVIDIA Hopper GPUs [2][4] - The service is offered at a significantly lower cost compared to traditional cloud service providers, making it an attractive option for various use cases [2][5] - Organizations can deploy enterprise-grade applications in a Nebius virtual private cloud environment, ensuring compliance with IT security standards and access to cost-efficient GPUs [3][6] Group 2: User Accessibility - Individuals and teams can join over 100,000 users of Saturn Cloud's hosted tiers, enabling quick sign-up and deployment of cloud resources with competitive pricing [4][5] - The solution allows teams to leverage Nebius's AI Cloud without the need for long-term investments in computing systems, with options for capacity reservations to enhance cost savings [5][6] Group 3: Strategic Impact - The collaboration aims to democratize access to enterprise-grade AI capabilities for organizations of all sizes, enabling them to become AI-driven without significant financial or engineering investments [6][7] - The integration of NVIDIA AI Enterprise software enhances Saturn Cloud's MLOps platform, providing advanced tools for AI development and deployment [6][10] Group 4: Company Background - Nebius is a technology company focused on building full-stack cloud infrastructure for the global AI industry, with a presence in Europe, North America, and Israel [9][10] - Saturn Cloud is recognized as the only MLOps platform offering multi-cloud GPU access on demand, simplifying the management of machine learning models at scale [10]