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 Inference as a Service (IaaS)
Rafay-powered Inference as a Service (IaaS) enables providers and enterprises to deploy, scale, and monetize GPU-powered inference endpoints optimized for large language models (LLMs) and generative AI applications.
Traditional inference environments often face challenges—static GPU allocation wastes capacity, idle costs accumulate, and manual management limits scalability. Rafay removes these constraints by enabling self-service inference APIs, elastic scaling, and built-in governance for predictable performance and sovereignty.
Organizations can offer LLM-ready inference services powered by vLLM, complete with Hugging Face and OpenAI-compatible APIs, to serve production workloads securely and efficiently.
Instant Deployment: Launch vLLM-based inference services in seconds through a self-service interface.
GPU-Optimized Performance: Leverage memory-efficient GPU utilization with dynamic batching and offloading.
Elastic Scaling: Scale inference endpoints seamlessly across GPU clusters for consistent throughput.
Simplify Inference Management at Scale
Rafay enables organizations to manage AI inference workloads at scale while maintaining high performance, compliance, and cost efficiency
vLLM Runtime Integration
Use vLLM’s optimized runtime to serve large models with low latency and high throughput.
Distributed Inference Scaling
Scale workloads across GPUs and nodes with automatic balancing.
API Compatibility
Support Hugging Face and OpenAI-compatible endpoints for easy integration with existing AI ecosystems.
Governance and Policy Control
Enforce consistent performance and auditability through centralized management.
Deliver Production-Ready AI Inference with Governance and ROI
Expose inference endpoints as high-demand service SKUs to maximize GPU ROI.
Deliver self-service APIs with predictable latency, throughput, and elastic capacity.
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Offer compliant, in-region inference services with full governance and auditability.
Automate endpoint creation, scaling, and policy enforcement to reduce operational 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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Product
Dynamic Resource Allocation for GPU Allocation on Rafay's MKS (Kubernetes 1.34)
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2025-10-06
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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2025-09-13
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!
