Product
Unlock the Next Step: From Cisco AI PODs to Self-service GPU Clouds with Rafay
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ai-infrastructure
enterprises
gpu-cloud
gpu-paas
2025-10-20
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
Unlock Your AI Potential with Cisco and Rafay: Transform AI PODs into a Self-Service GPU Cloud
Cisco provides AI-optimized infrastructure. The Rafay Platform makes it usable across teams, tenants, and use cases in days.
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The CIO’s guide to scalable, compliant, and developer-ready AI deployment
Learn how the Rafay Platform "allows CIOs to align their AI strategies with national regulatory frameworks while maintaining global scalability and agility" in this analyst report.
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Rafay Named Outperformer in 2025 GigaOm Radar Report for Managed Kubernetes
The latest Radar Report from GigaOm, the Rafay Platform's "Managed Kubernetes" capability is ranked as an “Outperformer."
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Gartner® Report – Market Trend: CSPs’ Opportunity to Capitalize on AI Infrastructure Through GPU as a Service
CSPs can harness these strategic opportunities to capitalize on the AI-optimized IaaS market, projected at $80 billion in 2028.
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Building AI Value within Borders
“Rafay’s central orchestration platform facilitates efficient, self-service infrastructure and AI application management” writes Accenture | NVIDIA in 2025 Building AI Value Within Borders paper.
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GPU Cloud Evaluation Report
Learn how to accelerate the ROI of your GPU infrastructure by quickly delivering a fully functional GPU Cloud with the self-service workflows and infrastructure orchestration of the Rafay Platform.
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How Enterprise Platform Teams Can Accelerate AI/ML Initiatives
This paper explores the key challenges that organizations experience supporting these initiatives, as well as best practices for successfully leveraging Kubernetes to accelerate AI/ML projects.
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How Enterprise Platform Teams Can Accelerate AI/ML Initiatives
This paper explores the key challenges that organizations experience supporting these initiatives, as well as best practices for successfully leveraging Kubernetes to accelerate AI/ML projects.
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10 Multi-Tenancy Best Practices for Namespaces as a Service (NaaS)
The white paper promises to delve into Kubernetes multi-tenancy best practices, aiming to guide organizations in leveraging Kubernetes effectively for improved resource utilization and cost efficiency.
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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"
"We are able to deliver new, innovative products and services to the global market faster and manage them cost-effectively with Rafay"
"We are able to deliver new, innovative products and services to the global market faster and manage them cost-effectively with Rafay"
Most Recent Blogs
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Dynamic Resource Allocation for GPU Allocation on Rafay's MKS (Kubernetes 1.34)
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GPU/Neocloud Billing using Rafay’s Usage Metering APIs
Cloud providers offering GPU or Neo Cloud services need accurate and automated mechanisms to track resource consumption.
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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!
