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 AI Inference as a Service
Rafay-powered Inference as a Service enables providers and enterprises to deploy, scale, and monetize GPU-powered inference endpoints optimized for large language models (LLMs) and generative AI applications.
Organizations can deliver LLM-ready inference services using supported inference engines such as vLLM, NVIDIA Dynamo, NVIDIA NIM microservices, SageMaker, and NemoClaw.
They expose Hugging Face and OpenAI-compatible APIs, making it easy to serve production workloads securely and efficiently.
- Instant Deployment: Launch vLLM-based inference services in minutes through a self-service interface.
- GPU-Optimized Performance: Leverage efficient GPU utilization through vLLM's optimized runtime and configurable resource allocation.
- Flexible Scaling: Scale inference endpoints by adding replicas, with traffic load-balanced across them for consistent throughput.
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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.
Why Choose Rafay for AI Inference as a Service?
Whether you're building an internal AI platform or launching managed inference services as a GPU cloud provider, Rafay simplifies the deployment and operation of production-ready AI inference. We combine GPU orchestration, self-service provisioning, multi-tenancy, governance, and usage metering to help organizations deliver secure, scalable inference services with less operational overhead.
With Rafay, you can:
- Launch self-service inference services without building custom platforms.
- Deliver secure, multi-tenant environments with enterprise governance.
- Maximize GPU utilization through automated resource management.
- Support sovereign and air-gapped deployments for regulated industries.
- Simplify lifecycle management for inference infrastructure and AI workloads.
Why not build it yourself? Deploying production AI inference requires much more than serving a model. Teams must also manage GPU scheduling, scaling, API access, tenant isolation, governance, monitoring, and lifecycle operations. Rafay brings these capabilities together in a single platform, helping organizations launch and operate production-ready inference services faster while reducing operational complexity.
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.
Benefits of Rafay-Powered AI Inference as a Service
Faster AI Deployment
Launch production-ready inference endpoints in minutes rather than building and managing the infrastructure yourself.
Lower Operational Overhead
Automate provisioning, scaling, governance, and lifecycle management across inference workloads.
Better GPU Utilization
Maximize infrastructure efficiency through optimized scheduling, dynamic scaling, and resource sharing.
Enterprise Governance
Enforce policies, access controls, and compliance requirements across environments from a central platform.
Monetization Opportunities
Turn GPU infrastructure into revenue-generating inference services with self-service access and usage-based consumption models.
Production Readiness
Deliver reliable, scalable inference services with built-in automation, observability, and operational controls.
Common Use Cases of Our AI Inference Services
Rafay-powered AI Inference as a Service helps organizations deploy and manage inference workloads across a wide range of production AI use cases, including:
For Cloud Providers
For Enterprises
- Offer managed LLM APIs to enterprise customers through secure, self-service inference endpoints.
- Monetize GPU infrastructure by delivering hosted inference services with usage-based consumption models.
- Deliver sovereign AI services for customers with strict data residency, security, and compliance requirements.
- Launch differentiated AI offerings that complement GPU-as-a-Service and expand your AI service portfolio.
- Power AI assistants and copilots with scalable, production-ready inference services.
- Deploy private AI applications while maintaining centralized governance and policy controls.
- Run retrieval-augmented generation (RAG) workloads with GPU-accelerated inference for knowledge-intensive applications.
- Standardize AI inference across teams through self-service access, automation, and centralized operations.
FAQs
Find answers to common questions about our Rafay-powered inference services below.
What is inference as a service?
Inference as a Service is a managed cloud service that provides on-demand access to AI inference endpoints. It enables organizations to deploy, scale, and manage large language models (LLMs) and other AI models without building and operating the underlying GPU infrastructure.
Which LLMs are supported?
Rafay supports open-source and custom large language models that run on vLLM, including models available through Hugging Face. Organizations can deploy the models that best fit their performance, cost, and compliance requirements.
Can I route inference based on sovereignty requirements?
Yes. Rafay enables policy-driven inference routing based on data residency, sovereignty, compliance, latency, capacity, and cost requirements. Organizations can ensure that inference requests are served only from approved regions or infrastructure locations, helping meet regulatory obligations while maintaining performance and availability.
Does Rafay support OpenAI-compatible APIs?
Yes. Rafay-powered inference endpoints support OpenAI-compatible APIs, making it easier to integrate AI applications and tools without significant code changes.
Can I run my own models?
Yes. Organizations can deploy and manage their own supported AI models alongside open-source models, giving them full control over model selection, performance, and data governance.
How does Rafay support multi-tenant AI infrastructure?
We provide tenant isolation, role-based access controls, policy enforcement, and quota management that allow multiple teams, customers, or business units to securely share infrastructure while maintaining governance and operational consistency.
Featured Resources
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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.
