Operationalizing AI Fabrics with Aviz ONES, NVIDIA Spectrum-X, and Rafay
Discover the new AI operations model available to enterprises that enables self-service consumption and cloud-native orchestration for developers.
SERVICES YOU CAN LAUNCH WITH THE RAFAY PLATFORM
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
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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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“We are able to deliver new, innovative products and services to the global market faster and manage them cost-effectively with Rafay.”
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White paper
Hybrid Cloud Meets Kubernetes
Learn how to Streamline Kubernetes Ops in Hybrid Clouds with AWS & Rafay
Try the Rafay Platform for Free
See for yourself how to turn static compute into self-service engines. Deploy AI and cloud-native applications faster, reduce security & operational risk, and control the total cost of Kubernetes operations by trying the Rafay Platform!
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