The Definitive PaaS Reference Architecture for GPU Cloud Providers & Enterprises
Enterprises and Cloud Providers leverage the Rafay Platform to deliver self-service consumption of AI and cloud-native infrastructure to developers and data scientists.
By delivering a GPU PaaS™ experience, teams can:
- Allocate GPU infrastructure in a self-service manner.
- Programmatically offer various flavors and sizes of GPU and compute resources.
- Support out-of-the-box integrations with NVIDIA services in a turnkey fashion.
Trusted by leading enterprises, neoclouds and service providers
Enabling Self-Service Consumption of GPUs and AI Applications with Nvidia
Featured Resources
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.
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.
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."
Gartner® Report – Market Trend: CSPs’ Opportunity to Capitalize on AI Infrastructure Through GPU as a Service
CSPs can harness these strategic opportunities to capitalize on the AI-optimized IaaS market, projected at $80 billion in 2028.
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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.
GPU Cloud Evaluation Report
Learn how to accelerate the ROI of your GPU infrastructure by quickly delivering a fully functional GPU Cloud with the self-service workflows and infrastructure orchestration of the Rafay Platform.
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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.
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.
10 Multi-Tenancy Best Practices for Namespaces as a Service (NaaS)
The white paper promises to delve into Kubernetes multi-tenancy best practices, aiming to guide organizations in leveraging Kubernetes effectively for improved resource utilization and cost efficiency.
