Rafay provides the control plane for AI factories, handling orchestration, multi-tenancy, governance, and self-service access to AI infrastructure across cloud, on-prem, and sovereign environments.

Rafay is not a GPU manufacturer or model provider. Rafay provides an infrastructure orchestration and consumption platform that enables organizations to operate AI factories by turning AI infrastructure into a governed, self-service platform. Learn more about AI factories here: https://rafay.co/ai-and-cloud-native-blog/what-is-an-ai-factory

Rafay enables enterprise platform teams to deliver a PaaS experience for GPU resources, both on-premises and in the cloud. The platform offers a cost-effective alternative to services like Amazon SageMaker or Google VertexAI, providing ML workbenches with similar functionality. Rafay’s self-service model and hierarchical experience sharing allow platform teams to selectively offer compute and ML workbench experiences to different teams, optimizing access to expensive GPU resources. Additionally, the platform includes chargeback capabilities to ensure fair cost allocation among internal teams. This comprehensive approach simplifies AI/ML infrastructure management, accelerating enterprise adoption while maintaining cost control and resource efficiency

Rafay’s AI/ML platform is utilized by various organizations, particularly in the financial services sector. We’re also collaborating with major GPU vendors for specialized use cases. A notable public example of a company using our AI/GPU stack is MoneyGram, a global leader in cross-border P2P payments and money transfers.

Yes, Rafay provides infrastructure orchestration and workflow automation for cloud-native (Kubernetes) and AI use cases for enterprises, cloud providers, neoclouds, and Sovereign AI clouds. Rafay helps companies deploy a Platform-as-a-Service (PaaS) experience that supports both CPU-only and GPU-accelerated compute environments. Platform teams can quickly set up and deliver customized self-service experiences for developers and data scientists, typically within days or weeks. This flexible platform allows end-users to easily access the computational resources they need, whether it’s standard CPU processing or more powerful GPU capabilities. Rafay’s solution streamlines the deployment and management of diverse computing environments, making it easier for organizations to support a wide range of applications, from standard software to complex AI/ML projects.

Rafay provides infrastructure orchestration and workflow automation for enterprises, cloud providers, neoclouds, and sovereign AI clouds. The Rafay Platform delivers a Platform-as-a-Service (PaaS) experience that enables companies to create customized compute environments for developers and data scientists. Rafay’s platform enables faster development and deployment of new capabilities while maintaining necessary controls and guardrails. By simplifying the process of implementing complex platforms, Rafay reduces the need for large teams of experts. In essence, Rafay streamlines cloud-native and AI/ML adoption by offering a ready-to-use platform that balances speed, efficiency, and security for businesses.

AI factories are used by enterprises, cloud service providers, and sovereign AI clouds that need to scale AI workloads efficiently, maximize GPU utilization, and deliver AI as a production service rather than isolated projects. You can see how Rafay worked with Canadian telecommunications provider Telus in this case study.

Rafay applies its proven governance and control features, originally developed for cloud-native projects, to AI/GPU initiatives. These capabilities include blueprinting, access management, chargebacks, and auditing/logging. This approach ensures that enterprises can maintain compliance and control over their AI projects, just as they do with other cloud-native initiatives. By leveraging these established features, Rafay helps organizations accelerate AI adoption while maintaining the necessary governance standards, ultimately leading to increased revenues and lower total cost of ownership for both cloud-native and AI/ML projects.

Rafay applies its proven governance and control features, originally developed for cloud-native projects, to AI/GPU initiatives. These capabilities include blueprinting, access management, chargebacks, and auditing/logging. This approach ensures that enterprises can maintain compliance and control over their AI projects, just as they do with other cloud-native initiatives. By leveraging these established features, Rafay helps organizations accelerate AI adoption while maintaining the necessary governance standards, ultimately leading to increased revenues and lower total cost of ownership for both cloud-native and AI/ML projects.

Yes, self-service compute platforms incorporate robust security measures. These include access controls, encryption, and compliance with industry standards. Your data and resources are protected throughout the process.