Sovereign Cloud Infrastructure for AI

Cloud providers, neoclouds and Sovereign AI clouds who have partnered with Rafay are leading the charge to deliver CSP-grade use cases to their user communities. From agentic applications, ML workbenches, models as a service, to highly tuned virtual machines, K8s clusters and baremetal servers, Rafay is fast becoming the global partner of choice for the most innovative GPU providers in the world.

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For Sovereign Clouds

Empower Local Innovation with Secure, Compliant AI Infrastructure

Full lifecycle management of Kubernetes, GPUs, and bare metal servers
Secure, multi-tenant environments with built-in governance
Model catalog and fine-tuning workflows
Partner-ready portals and marketplace integration

Supporting Features

Sovereign-Ready Orchestration Accelerates AI Application Delivery

Enable usage-based pricing with auditable GPU and AI resource metering
Deliver branded, multi-tenant portals for sovereign AI users
Serverless & dedicated inferencing at the edge or core
Deploy in air-gapped or restricted environments with strict access controls

Key Benefits Sovereign Cloud Providers Can Expect with the Rafay Platform

Orchestrate across any environment or app

Provide the infrastructure elasticity, scalability, automation and reliability needed for high efficiency AI application delivery.

Invest in domestic infrastructure

Government and national-issue driven organizations can advance their own economies safely.

Control compliance

Industry leaders can control compliance across their value chains with AI solutions tailored to industry-specific needs.

Achieve faster time-to-value

Scalable infrastructure is now enforced with the necessary sovereign controls on data, models and agents.

FAQs

Questions relevant to sovereign clouds and how Rafay can support AI factory initiatives to accelerate AI infrastructure needs.

Who uses AI factories?

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.

What role does Rafay play in AI factories?

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.

Is Rafay an AI factory?

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.

How does Rafay's platform streamline AI/ML infrastructure management for enterprise adoption?

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

How does Rafay’s platform accelerate time-to-value for AI/ML projects?

Without Rafay, platform teams implement complex platforms internally over multiple years and with large teams of experts.With Rafay, platform teams can deliver a finely tuned PaaS experience to internal users in weeks.

Who uses Rafay's platform for AI/ML initiatives?

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.

Does Rafay offer a GPU PaaS?

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.

What exactly does Rafay do or provide around AI/ML or cloud-native adoption?

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.