# **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

[**Operationalizing AI Fabrics with Aviz ONES, NVIDIA Spectrum-X, and Rafay**](https://info@rafay.co/white-papers/operationalizing-ai-fabrics-with-aviz-ones-nvidia-spectrum-x-rafay)

[**The Definitive GPU PaaS Reference Architecture**](https://info@rafay.co/nvidia-reference-architecture)

[**Unlock Your AI Potential with Cisco and Rafay: Transform AI PODs into a Self-Service GPU Cloud**](/content/resources/white-papers/cisco-rafay-ai-pods-gpu-cloud/index.html)

[**The CIO’s guide to scalable, compliant, and developer-ready AI deployment**](https://info@rafay.co/research/silicon-media-analyst-report)

[**Rafay Named Outperformer in 2025 GigaOm Radar Report for Managed Kubernetes**](/content/resources/white-papers/rafay-named-outperformer-in-2025-gigaom-radar-report-for-managed-kubernetes/index.html)

[**Building AI Value within Borders**](/content/resources/accenture-whitepaper/index.html)

[**GPU cloud evaluation report**](https://info@rafay.co/resources/white-papers/gpu-cloud-evaluation-report)

[**How Enterprise Platform Teams Can Accelerate AI/ML Initiatives**](/content/resources/white-papers/how-enterprise-platform-teams-accelerate-ai-ml-initiatives/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

## Start a conversation with Rafay

Talk with Rafay experts to assess your infrastructure, explore your use cases, and see how teams like yours operationalize AI/ML and cloud-native initiatives with self-service and governance built in.

[Start a Conversation](https://info@rafay.co/start)