Simplify Cloud-Native and AI Infrastructure Complexity with Enterprise-Grade Governance
The Rafay Platform’s multi-tenant foundation speeds up innovation through faster experimentation, faster shipment of new capabilities and features, reduction in infrastructure waste, increased utilization and the manifestation of better business outcomes. With Rafay, platform engineering teams deliver self-service consumption to developers and data scientists, enforce governance and control across hybrid environments, and keep infrastructure costs low.
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Run Compliant Cloud-Native and AI Workloads in Private Clouds Without Slowing Development
Most enterprises face a trade-off: meet strict compliance requirements or move fast. The Rafay Platform makes both possible. With air-gapped deployments, multi-tenancy, and centrally governed self-service environments, enterprises can securely operationalize AI workloads in their own data centers—while still giving internal teams the agility of a modern cloud experience.
Slash Cloud Complexity and Costs in AWS, Azure, or GCP
Workloads in the public cloud create new challenges: spiking infrastructure costs, governance gaps, and operational drag. The Rafay Platform abstracts away the complexity, giving platform teams a single pane of glass to orchestrate, govern, and optimize AI/ML infrastructure across clouds. Enterprises cut costs, reduce overhead, and give developers the freedom to innovate faster.
Trusted by leading enterprises, neoclouds and service providers
One Platform - Multiple Deployment Options
Deploy as a SaaS
A majority of Rafay customers consume Rafay in a SaaS form factor. Why? Because the SaaS model lets them start immediately with the Rafay Platform and deliver value to their customers. The Rafay platform is SOC-2 Type compliant, and will address all requirements put forward by your security team.
Deploy in an Air-Gapped Model
Customers in highly regulated industries prefer Rafay’s air-gapped controller model. Team Rafay is ready to help you deploy the Rafay Platform in your data center or in your private/public cloud environment. You get exactly the same experience and all the same features available to our SaaS customers.
Deploy across Data Center and CSP Environments
Whether you plan to deploy GPUs in multiple colos, or lease GPUs in a CSP environment, or both, Rafay can help. With Rafay, all your compute across all private and CSP environments can be managed as a single pool of GPUs and CPUs, reducing operational overhead and enabling cloud-bursting use cases.
Featured Resources
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Building AI Value within Borders
“Rafay’s central orchestration platform facilitates efficient, self-service infrastructure and AI application management” writes Accenture | NVIDIA in 2025 Building AI Value Within Borders paper.
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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.
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Rafay Named Outperformer in 2025 GigaOm Radar Report for Managed Kubernetes
The latest Radar Report from GigaOm, the Rafay Platform's "Managed Kubernetes" capability is ranked as an “Outperformer."
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Unlock Your AI Potential with Cisco and Rafay: Transform AI PODs into a Self-Service GPU Cloud
Cisco provides AI-optimized infrastructure. The Rafay Platform makes it usable across teams, tenants, and use cases in days.
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The CIO’s guide to scalable, compliant, and developer-ready AI deployment
Learn how the Rafay Platform "allows CIOs to align their AI strategies with national regulatory frameworks while maintaining global scalability and agility" in this analyst report.
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Rafay Named Outperformer in 2025 GigaOm Radar Report for Managed Kubernetes
The latest Radar Report from GigaOm, the Rafay Platform's "Managed Kubernetes" capability is ranked as an “Outperformer."
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Questions and answers about GPU PaaS™ for Enterprises
Find answers to common questions about our GPU Cloud Orchestration services below.
Yes. GPU and Sovereign Cloud providers can choose to offer fractional GPUs to end users in a self-service fashion. The Rafay Platform will take care of security, compute isolation and chargeback data collection.
Yes. The Rafay Platform offers a variety of workbenches out of the box. These are based on Kubeflow and KubeRay, with end users consuming these platforms “as a service,” without needing to configure or operate any of these tools on their own. Further, the Rafay platform provides a low-code/no-code framework that empowers partners to bring new capabilities to market faster, e.g. verticalized agents, co-pilots, document translation services, and more.
Yes. The Rafay Platform has always supported CPU-based workloads and can easily deliver a PaaS experience that offers CPU+GPU instances to end users.
The Rafay Platform collects granular chargeback information that can easily be exported to the customer’s billing systems for downstream dissemination. Chargeback group definition and data collection can be carried out programmatically.
Yes. Rafay supports a number of IaC frameworks, enabling customers to programmatize every aspect of their cloud. The Platform supports Terraform, OpenTofu, GitOps pipelines, CLI and API workflows out of the box.
How Platform Teams Can Accelerate AI/ML Initiatives
Delve into best practices for successfully leveraging Kubernetes and cloud operations to accelerate AI/ML projects.
