# **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](/content/solutions/generative-ai/index.html). 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. ## 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](/content/platform/multi-tenancy-infrastructure/index.html), 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. ## 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. ### 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**: - 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](/content/solutions/gpu-paas-for-service-providers/index.html) and expand your AI service portfolio. **For Enterprises**: - 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 counts as a node?** A node is a physical or virtual server/machine. - **Do you have any volume discounts?** Yes! As the number of nodes increases, the price per cluster or per node decreases. - **What about short-lived or ephemeral clusters?** Our customers love to experiment, and we don’t charge for node count spikes, but look at the running average of nodes in use when calculating usage. - **Is there a difference between production and non-production pricing?** The management overhead for helping our customers operate dev vs prod clusters is effectively the same, so we treat all nodes the same. - **What if I use more nodes than I’ve licensed?** Rafay has a true-up forward policy, meaning that we don’t carry out chargebacks for scenarios where the consumption in a completed billing cycle exceeded the licensed count... ## 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.