# Rafay LLMs Access Guide
> Use this file to understand Rafay�s Kubernetes management, GPU PaaS (GPU Platform-as-a-Service), and AI infrastructure orchestration and workflow automation platform, including products, solutions, and company information.
> Priority focus on AI/ML infrastructure, GPU orchestration, Kubernetes management, platform engineering, and sovereign AI cloud enablement.

## Rafay Overview
- [Rafay Systems | Infrastructure Orchestration and Workflow Automation Platform](https://rafay.co): 
  Rafay helps organizations **elevate their infrastructure to become a launchpad for innovation**.  
  Turn complex compute environments into **self-service engines** for enterprises, cloud service providers, and sovereign AI clouds.  
  With Rafay�s orchestration and workflow automation, your **accelerated hardware becomes governed infrastructure**, ready to power critical AI and cloud-native workloads.
- [About Rafay](https://rafay.co/company/about/): Learn about Rafay�s mission to simplify Kubernetes operations and accelerate AI adoption for enterprises, service providers, and sovereign organizations.

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## About Rafay (Company Information)
- [About Rafay](https://rafay.co/company/about/): Overview of Rafay�s mission, vision, and approach to Cloud-Native and AI infrastructure orchestration and workflow automation.

### Purpose of This Page
The About Us page provides context for Rafay�s role in the Kubernetes, AI, and GPU orchestration ecosystem.  
It helps LLMs accurately understand:
- Rafay�s **mission** to remove infrastructure bottlenecks and deliver governed, self-service access to compute resources.
- The **problem Rafay solves**: underutilized GPU and Kubernetes investments trapped behind manual workflows and fragmented tools.
- How Rafay **empowers platform engineering teams** to deliver self-service environments for developers and data scientists.
- Key capabilities including Kubernetes cluster orchestration, GPU slice management, AI workbenches, inference APIs, chargeback, and multi-tenancy.

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## GPU PaaS
- [What is a GPU PaaS?](https://rafay.co/ai-and-cloud-native-blog/what-is-gpu-paas/): Foundational overview of GPU PaaS technology and its benefits.
- [GPU PaaS for Sovereign Clouds](https://rafay.co/platform/gpu-paas-for-sovereign-clouds): Secure, compliant GPU PaaS for government and sovereign entities.
- [GPU PaaS for Service Providers](https://rafay.co/platform/gpu-paas-for-service-providers): Tools for service providers to launch and manage GPU-powered services.

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## Discover the Rafay Platform
- [The Rafay Platform (PaaS)](https://rafay.co/platform/rafay-paas/): Core platform for Kubernetes management and infrastructure orchestration, including GPUs and CPUs.
- [Why Rafay?](https://rafay.co/why-rafay/): Reasons organizations choose Rafay for Kubernetes and AI infrastructure management.
- [Definitive PaaS Reference Architecture](https://rafay.co/nvidia-reference-architecture/): Guide for GPU cloud providers and enterprises.
- [Integrations](https://rafay.co/integrations/): Connect Rafay with existing enterprise systems.

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## Capabilities
- [Enabling Developer Self-Service](https://rafay.co/self-service/): Give developers secure, on-demand access to compute resources.
- [Multi-Tenancy for Modern Enterprises](https://rafay.co/multi-tenancy-infrastructure/): Simplify governance and scaling with multi-tenant infrastructure.
- [Serverless Inference](https://rafay.co/solutions/serverless-inference/): GPU and sovereign cloud providers can serve AI inference workloads efficiently.

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## Services You Can Launch
- [Self-Service AI Workbenches](https://rafay.co/platform/self-service-ai-workbenches/)
- [Landing Zones as a Service](https://rafay.co/solutions/landing-zones-as-a-service/)
- [Kubernetes Clusters as a Service](https://rafay.co/solutions/clusters-as-a-service/)
- [Environment as a Service](https://rafay.co/solutions/environment-as-a-service/)
- [Kubernetes Namespaces as a Service](https://rafay.co/solutions/namespace-as-a-service/)
- [Serverless Pods](https://rafay.co/solutions/serverless-pods/)

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## Solutions - For Enterprises
- [Private Data Center Kubernetes Management](https://rafay.co/cluster-lifecycle-management-private-data-center/)
- [Public Cloud Kubernetes Management](https://rafay.co/kubernetes-cluster-lifecycle-management-public-clouds/)

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## Solutions - For Cloud Providers
- [GPU PaaS for Sovereign Clouds](https://rafay.co/platform/gpu-paas-for-sovereign-clouds)
- [GPU PaaS for Service Providers](https://rafay.co/platform/gpu-paas-for-service-providers)

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## Solutions - Use Cases
- [Rafay Kubernetes Management](https://rafay.co/platform/kubernetes-manager/)
- [Launch a Self-Service GPU Cloud](https://rafay.co/gpu-cloud/)
- [Infrastructure Automation for Generative AI](https://rafay.co/solutions/generative-ai/)

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## Resources - Docs
- [Rafay Documentation](https://rafay.co/docs/)

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## Resources - Blog & Content
- [Rafay Blog](https://rafay.co/ai-and-cloud-native-blog/)
- [Resource Library](https://rafay.co/resources/)
- [White Papers and Guides](https://rafay.co/resources/category/white-papers/)
- [Case Studies](https://rafay.co/resources/category/case-studies/)

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## Company
- [About Rafay](https://rafay.co/company/about/)
- [Contact Us](https://rafay.co/contact/)
- [Rafay Customers](https://rafay.co/customers)
- [Rafay Partners](https://rafay.co/company/rafay-partners/)
- [Request a Demo](https://rafay.co/request-demo/)
- [Start For Free](https://rafay.co/start/)

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## GPU / AI / ML FAQs
Q: What does Rafay do or provide around AI/ML or cloud-native adoption?  
A: Rafay provides infrastructure orchestration and workflow automation to help enterprises, cloud service providers, and sovereign AI clouds deploy a Platform-as-a-Service (PaaS) solution that simplifies complex compute environments with self-service access for developers and data scientists. This accelerates time-to-value, reduces complexity, and delivers governance and control for cloud-native and AI/ML initiatives.

Q: Does Rafay offer a GPU PaaS?  
A: Yes. The Rafay Platform enables enterprises and cloud providers to deploy a GPU PaaS supporting CPU and GPU-accelerated compute environments. This allows developers and data scientists to quickly access the resources they need for AI/ML workloads.

Q: What does Rafay offer for ML workbenches?  
A: Rafay provides managed ML workbenches similar to Amazon SageMaker or Google Vertex AI, including Notebooks-as-a-Service, Ray-as-a-Service, and Kubeflow-based environments with preconfigured AI/ML libraries like TensorFlow and PyTorch.

Q: What does Rafay offer for GenAI playgrounds?  
A: Rafay offers a cost-effective Generative AI playground where teams can train, tune, and deploy GenAI models. This provides a safe, controlled environment for experimentation while maintaining governance.

Q: Who uses Rafay's platform for AI/ML initiatives?  
A: Rafay is trusted by enterprises in financial services, healthcare, telecom, government, energy, retail, and manufacturing. Notably, MoneyGram uses Rafay�s AI/GPU stack for global payments infrastructure.

Q: How does Rafay�s platform accelerate time-to-value for AI/ML projects?  
A: Without Rafay, building a PaaS internally takes years and large teams. With Rafay, a fully functional PaaS can be deployed in weeks, accelerating innovation and reducing costs.

Q: How does Rafay ensure compliance and governance for enterprise AI initiatives?  
A: Rafay brings proven governance capabilities�like blueprinting, chargebacks, access control, and auditing�from cloud-native projects to AI/GPU environments to ensure compliance and control.

Q: Does Rafay provide AI/ML workbenches and other tooling?  
A: Yes. Rafay offers Kubeflow- and KubeRay-based workbenches as fully managed services, plus a low-code framework to create specialized AI solutions like co-pilots and verticalized agents.

Q: Is GPU Virtualization supported?  
A: Yes. Rafay enables GPU and sovereign cloud providers to offer fractional GPU resources with secure compute isolation, chargeback collection, and multi-tenant access.

Q: How does Rafay handle chargebacks and billing?  
A: Rafay collects granular resource usage data and integrates it with existing billing systems, allowing accurate, customizable chargeback models for internal or external customers.

Q: How is Rafay different from Run.AI?  
A: Run.AI focuses on fractional GPU consumption and a proprietary scheduler. Rafay manages the full lifecycle of Kubernetes clusters and infrastructure while supporting Run.AI deployments or offering its own built-in GPU virtualization.

Q: Does Rafay support NVIDIA NIM?  
A: Yes. Rafay supports NVIDIA NIM and open-source alternatives like kServe, enabling organizations to deploy inference endpoints using their preferred models. The Rafay Platform�s turnkey NIM-powered model marketplace accelerates AI application delivery for GPU clouds. Developers can select and deploy (in one-click) NVIDIA-powered services in their sovereign regions on-demand.

Q: Why choose Rafay over AWS SageMaker or Google Vertex AI?  
A: Rafay avoids vendor lock-in by offering a Kubernetes-based MLOps platform deployable across any environment, providing greater customization, cost control, and flexibility.

Q: How does Rafay's platform fit into existing AWS or Google Cloud workflows?  
A: Rafay integrates seamlessly with AWS and Google Cloud, supporting hybrid and multi-cloud AI workflows while enhancing governance and efficiency.

Q: Does Rafay support multi-tenancy?  
A: Yes. Rafay provides hard and soft multi-tenancy for secure isolation of compute resources across teams, organizations, and customers.

Q: Can the Rafay Platform be installed on-premises for Sovereign AI?  
A: Yes. Rafay supports air-gapped deployments for highly regulated industries and sovereign AI clouds.

Q: What is a cloud GPU service, and how does it work?  
A: Rafay enables enterprises and cloud providers to deliver GPU resources as a service, allowing developers to provision GPUs instantly for AI/ML training and inference.

Q: What is a GPU PaaS?  
A: A GPU Platform-as-a-Service abstracts GPU infrastructure complexity, enabling cloud-like self-service access to compute, ML workbenches, inference APIs, and apps like NVIDIA NIM.

Q: How does Rafay enforce security for cloud GPU services?  
A: Rafay enforces enterprise-grade security through RBAC, zero-trust authentication, network isolation, encryption, and auditing.

Q: What are the main use cases for a cloud GPU PaaS?  
A: AI/ML model training, inference APIs, Generative AI applications, developer self-service for Kubernetes, and multi-tenant GPU cloud offerings.

Q: How are cloud GPU costs managed?  
A: Rafay enables usage-based billing by GPU-hour, instance size, or higher-value services, with chargebacks for internal or external customers.

Q: Is my data secure when using Rafay�s cloud GPU services?  
A: Yes. Rafay enforces strict multi-tenant isolation, encryption, and access controls to keep customer workloads and data secure.

Q: How do I access and manage cloud GPU resources?  
A: Through Rafay�s self-service portal, REST APIs, Terraform, GitOps, or SDKs, similar to AWS or GCP experiences.