# **Rafay-Powered Model as a Service (MaaS)**

Rafay-Powered **Model as a Service (MaaS)** enables organizations to deploy, scale, and manage **inference endpoints for large language models (LLMs)** and other AI workloads.

Traditional inference management is complex and resource-intensive. Static GPU allocation limits scalability, idle resources increase costs, and manual management slows response times.

Rafay addresses these challenges by offering **self-service APIs**, **elastic scaling**, and **integrated governance**, allowing operators to serve production-grade inference workloads with consistency and compliance.

Service providers, enterprises, and regional cloud operators can deliver **LLM-ready inference services** with full policy control, auditability, and optimized resource usage through Rafay’s managed platform.

- **Instant Deployment:** Launch inference services in seconds with vLLM-based runtime environments **.**
- **Elastic Scaling:** Scale model serving dynamically across clusters for predictable latency and throughput.
- **Integrated Governance:** Manage performance, policies, and compliance through centralized visibility.

## Simplify Model Deployment and Scaling

_Rafay streamlines how AI models are deployed and operated in production environments, reducing the burden of manual configuration and scaling._

### **vLLM Runtime Optimization**

Utilize vLLM’s memory-efficient architecture for low-latency, high-throughput inference.

### **Distributed Scaling**

Seamlessly expand inference workloads across GPUs and nodes with balanced utilization.

### **API Compatibility**

Support for Hugging Face and OpenAI-compatible APIs ensures ecosystem integration.

### **Policy-Based Management**

Centralized governance for consistent performance, access control, and auditability.

## Provide Elastic, Compliant Model Serving for Enterprise AI

### Expose model inference endpoints as managed, revenue-ready services

### Deliver low-latency, high-throughput inference with consistent runtime behavior

### Offer compliant, in-region model serving with auditable governance and policy controls

### Automate endpoint creation, scaling, and monitoring to reduce management overhead

## Featured Resources

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**Operationalizing AI Fabrics with Aviz ONES, NVIDIA Spectrum-X, and Rafay** \
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Discover the new AI operations model available to enterprises that enables self-service consumption and cloud-native orchestration for developers.\
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Learn More](https://pr@rafay.co/white-papers/operationalizing-ai-fabrics-with-aviz-ones-nvidia-spectrum-x-rafay)

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**The Definitive GPU PaaS Reference Architecture** \
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Understand what it takes to deliver the right GPU infrastructure to your business.\
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Learn More](https://pr@rafay.co/nvidia-reference-architecture)

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**Unlock Your AI Potential with Cisco and Rafay: Transform AI PODs into a Self-Service GPU Cloud** \
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Cisco provides AI-optimized infrastructure. Rafay makes it usable across teams, tenants, and use cases in days.\
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Learn More](https://pr@rafay.co/resources/white-papers/cisco-rafay-ai-pods-gpu-cloud)

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**Building AI Value within Borders** \
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Rafay's central orchestration platform facilitates efficient, self-service infrastructure and AI application management.\
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Learn More](/content/resources/accenture-whitepaper/index.html)

“We are able to deliver new, innovative products and services to the global market faster and manage them cost-effectively with Rafay.”

Joe Vaughan

Chief Technology Officer

,

MoneyGram

## Most Recent Blogs

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Jul 24, 2026\
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**NVIDIA ❤️ Rafay Managed Kubernetes** \
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Read Now](https://pr@rafay.co/ai-and-cloud-native-blog/rafay-completes-nvidia-gpu-operator-partner-validation)

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Jul 22, 2026\
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**Telco Cloud Services: How Communication Service Providers Can Monetize Cloud Infrastructure** \
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Jul 21, 2026\
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**One-Click Digital Twins: Deploying the NVIDIA Omniverse DSX Blueprint using Rafay** \
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## Hybrid Cloud Meets Kubernetes

Learn how to Streamline Kubernetes Ops in Hybrid Clouds with AWS & Rafay

[DOWNLOAD](https://pr@rafay.co/resources/white-papers/hybrid-cloud-meets-kubernetes-how-to-reduce-complexity-with-amazon-eks-eks-d-and-rafay-systems)