Rafay-Powered Model as a Service | 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.