AI & ML FAQs | Rafay AI Infrastructure Platform

GPU/AI/ML FAQs

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 ding them for it. 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.

How much does Enterprise Support (24x7x365) cost?

Enterprise Support is available at an additional fee equaling 20% of the cluster or node subscription.

Do you have EDU or GOV discounts?

Yes, please contact sales for more information about discounts for educational institutions and government agencies.

What does Rafay do or provide around AI/ML or cloud-native adoption?

Rafay provides infrastructure orchestration and workflow automation for enterprises, cloud providers, neoclouds, and sovereign AI clouds. The Rafay Platform delivers a Platform-as-a-Service (PaaS) experience that enables companies to create customized compute environments for developers and data scientists.

Does Rafay offer a GPU PaaS?

Yes, Rafay provides infrastructure orchestration and workflow automation for cloud-native (Kubernetes) and AI use cases.

What does Rafay offer for ML workbenches?

Rafay provides curated ML workbenches that offer developers and data scientists an experience similar to Amazon SageMaker or Google VertexAI, but at a more competitive price point.

What does Rafay offer for GenAI playgrounds?

Rafay provides a controlled, cost-effective Generative AI playground for organizations new to GenAI, allowing data scientists to train, tune, and serve GenAI models.

Who uses Rafay's platform for AI/ML initiatives?

Rafay’s AI/ML platform is utilized by various organizations, particularly in the financial services sector.

How does Rafay’s platform accelerate time-to-value for AI/ML projects?

Without Rafay, platform teams implement complex platforms internally over multiple years and with large teams of experts.

How does Rafay ensure compliance and governance for enterprise AI initiatives?

Rafay applies its proven governance and control features to AI/GPU initiatives including blueprinting, access management, chargebacks, and auditing/logging.

How does Rafay's platform streamline AI/ML infrastructure management for enterprise adoption?

Rafay enables enterprise platform teams to deliver a PaaS experience for GPU resources, both on-premises and in the cloud.

Does Rafay provide AI/ML workbenches and other tooling?

Yes, Rafay offers a comprehensive suite of AI/ML tools, including Kubeflow and KubeRay.

Is GPU virtualization supported?

Yes. The Rafay Platform supports three GPU sharing modes.

How does Rafay solve for chargeback and billing?

Rafay offers a comprehensive solution for chargebacks and billing, collecting granular chargeback information on resource usage.

How does Rafay differ from Run.AI?

Rafay provides a more comprehensive platform that manages the full lifecycle of underlying Kubernetes clusters and environments.

Does Rafay support NVIDIA NIMs/NIM?

Yes, Rafay supports NVIDIA Inference Microservices (NIM).

Why consider Rafay's solution over AWS SageMaker or Google Vertex AI?

Rafay’s solution offers vendor agnosticism and greater customizability.

How does Rafay's solution fit into existing AWS/Google Cloud workflows?

Rafay’s MLOps platform is designed to seamlessly integrate with existing cloud ecosystems.

Will managing Kubernetes and Kubeflow add complexity compared to fully managed services?

Rafay’s platform simplifies management processes associated with Kubernetes.

What about the cost? Are there hidden expenses in managing our own infrastructure?

Rafay aims to provide transparent and potentially cost-saving solutions.

What's Rafay's stance on support and reliability?

Rafay is committed to providing enterprise-grade support and reliability.

How do Rafay's GPU PaaS and MLOps offerings benefit an AWS sales team?

Rafay’s offerings complement AWS services in enhancing their ecosystem.

What integrations does Rafay support, and what SLA and roadmap information is available?

Rafay integrates with existing cloud and enterprise technologies.

Does Rafay offer customizable solutions and professional services for industry and compliance needs?

Yes, Rafay supports custom environment templates and professional services.

What hardware/software vendor partnerships are critical for Rafay's AI platform?

Rafay's ecosystem aligns with NVIDIA and key infrastructure partners such as Cisco and Dell.

What are Rafay's deployment requirements and air-gapped capabilities?

Rafay supports multiple deployment models including fully managed SaaS and air-gapped deployments.

Can Rafay provide specific ROI examples or case studies?

Yes. Rafay's materials cite significant customer outcomes.

How does Rafay differentiate itself from competitors, hyperscalers, and in-house builds?

Rafay combines various capabilities into one platform that helps organizations operationalize AI infrastructure.

What is the typical implementation timeline?

Rafay supports rapid time-to-value, with customer examples of launching self-service GPU clouds in days.

Does Rafay offer free trials, POC programs, or sandbox environments?

Rafay can support proofs of concept or pilot evaluations.

How is the Rafay Platform priced, and what are the available pricing tiers?

Rafay pricing depends on various factors and requires contact with sales for a tailored quote.

What professional services, training programs, and onboarding does Rafay offer?

Rafay provides services to help customers implement the Rafay Platform.

Can Rafay help with cost optimization, and what reporting and analytics does it provide?

Yes, Rafay helps optimize costs and provides reporting and analytics for resource usage.

How does Rafay help with Kubernetes cluster lifecycle management?

Rafay supports Kubernetes lifecycle management across various environments.

How does the Rafay Platform ensure governance, compliance, and standardization?

Rafay enforces governance through various controls and compliance features.

How does the Rafay Platform enable self-service for developers and data scientists?

Rafay enables developers to launch approved infrastructure through various interfaces.

What are the key features and capabilities of Rafay's AI Token Factory?

Key Token Factory capabilities include token-metered usage and multi-tenant AI service delivery.

What is Rafay's AI Token Factory, and how does it monetize AI services?

Rafay's AI Token Factory converts GPU inference infrastructure into governed, token-metered AI services.

How does Rafay enable and orchestrate AI factories?

Rafay helps build and run AI factories by turning GPU infrastructure into self-service AI platforms.

What are the key capabilities, solutions, and types of services that can be launched with the Rafay Platform?

Organizations can package infrastructure into consumable services across GPU and AI workloads.

Who is the target audience for the Rafay Platform?

Rafay serves enterprises, GPU cloud providers, NeoClouds, and sovereign AI cloud operators.

What are the general benefits of using the Rafay Platform?

The Rafay Platform helps organizations accelerate infrastructure consumption and enforce governance.

What core problems does the Rafay Platform address for businesses?

Rafay addresses gaps in infrastructure usability, provisioning, and governance.

What is the Rafay Platform?

The Rafay Platform helps turn GPU and CPU infrastructure into consumable AI services.

What is the relationship between AI Token Factory and a Token Delivery Network?

AI Token Factory is a capability enabling a Token Delivery Network.

How does Rafay help operators move from GPU infrastructure to AI services?

Rafay aids the transformation of GPU infrastructure into self-service AI platforms.

What is a Token Delivery Network?

A Token Delivery Network is a distributed architecture for delivering AI inference.

What is serverless inferencing?

Serverless inference allows dynamic deployment of AI models without managing infrastructure.

What is an AI inference platform?

An AI inference platform is a scalable environment for managing AI models in production.

What is an inference engine in AI?

An inference engine generates predictions using trained AI models.

What is a token in AI?

A token in AI is a unit of text processed by a language model.

What role does Rafay play in a Token Delivery Network?

Rafay provides the operational layer for managing model inference endpoints across networks.

How does Rafay keep data and operations within national or EU sovereignty boundaries?

Rafay operates within the operator's premises, ensuring data residency and isolation.

How does Rafay align with ENS and EU sovereignty frameworks?

Rafay supports defined security measures and regulatory certifications for compliance.

How quickly can a neocloud go from raw GPUs to billable services with Rafay?

A neocloud can reach its first billable AI services in approximately six to eight weeks.

What is the Build-Validate-Operate-Transfer (BVOT) methodology?

BVOT is Rafay's method for deploying and handing over a production AI cloud.

Is Rafay NVIDIA AI Cloud Ready and NVIDIA-validated?

Yes, Rafay is validated and compliant with NVIDIA reference architectures.

Can the Rafay Platform run fully air-gapped with no external connectivity?

Yes, it supports fully air-gapped deployment, suitable for classified environments.

Why are telcos positioned to lead Token Delivery Networks?

Telcos have necessary infrastructure and relationships to support AI service delivery networks.

What is token-metered AI service delivery?

Token-metered delivery measures AI service consumption at the token level for billing and governance.