Rafay

Visibility37

Vibe98

AI Visibility & Sentiment

Rafay

Rafay provides an enterprise-grade infrastructure orchestration and workflow automation platform designed for cloud-native and AI workloads. The platform enables organizations to transform complex compute environments into self-service consumption engines, supporting GPU cloud providers, enterprises leveraging private clouds, and those operating in public clouds.

Active Monitoring

rafay.co

Enterprise Software Startups

AI Visibility Score

37/100

Low

Sentiment Score

98/100

AI Perception

Summary

Rafay has successfully established itself as a primary authority for AI Infrastructure Architects, achieving a dominant 75% mention rate and an elite 2.0 average position on Claude. However, this niche success is undercut by a total lack of visibility in high-volume multi-cloud and platform engineering queries where incumbent competitors like Terraform and AWS currently own the narrative. The brand is effectively a specialist 'hidden gem' that lacks the broad discoverability required to capture the wider enterprise Kubernetes market.

Value Proposition

Rafay simplifies infrastructure orchestration for cloud-native and AI workloads, enabling enterprises to achieve self-service consumption, full lifecycle management, and accelerated developer productivity across hybrid and multi-cloud environments.

Overview

Mission

Elevate infrastructure to become a launchpad for innovation by transforming complex compute environments into self-service consumption engines for enterprises.

Products & Services

Kubernetes Management PlatformGPU Cloud Infrastructure OrchestrationAI/ML Workload ManagementMulti-Cloud Cluster AutomationInfrastructure Lifecycle Management

Agent Breakdown

AI Platforms

How often do different AI platforms reference Rafay?

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Conversation Analysis

Topics

What conversations is Rafay included in — or excluded from?

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Buyer Personas

Personas

Who does each AI platform recommend Rafay to, and when?

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Programmatic Testing

Sample Conversations

We programmatically analyze questions that real customers are asking to AI agents and chatbots, extract brand mentions and sentiment, analyze every response, and synthesize the data into an action plan to increase AI visibility.

ChatGPT

Claude

Gemini

AI Overviews

AI And GPU Infrastructure Strategy(3 queries)

“we're starting to run way more AI models on-prem and in the cloud, what's the best way to manage all our GPU clusters without a huge manual overhead?”

1/4 platforms mentioned

ChatGPT

1.Kubernetes

2.NVIDIA GPU Operator

3.Cluster Autoscaler

4.Karpenter

5.Kubeflow

+36 more

Claude

1.NVIDIA GPU Operator

2.NVIDIA Triton Inference Server

3.Ray

4.Runai

5.Anduril

+6 more

Gemini

1.Kubernetes

2.NVIDIA GPU Operator

3.Amazon EKS

4.Google GKE

5.Azure AKS

+24 more

AI Overviews

1.NVIDIA Run:ai

2.ClearML

3.Rafay

4.NVIDIA GPU Operator

5.Prometheus

+12 more

“help me set up a self-service portal for my data scientists to grab GPU resources whenever they need them for training”

2/3 platforms mentioned

Claude

1.Karpenter

2.EKS

3.GKE Autopilot

4.GKE

5.Terraform

+9 more

Gemini

1.NVIDIA

2.Terraform

3.Crossplane

4.TensorFlow

5.PyTorch

28.Rafay

+24 more

AI Overviews

1.NVIDIA Run:ai

2.Kubeflow

3.Rafay AI Workbench

4.Lightning AI

5.DigitalOcean

+5 more

“what tools should I use for AI/ML workload management across a hybrid cloud setup?”

1/3 platforms mentioned

Claude

1.Kubeflow

2.EKS

3.GKE

4.Ray on Kubernetes

5.Terraform

+11 more

Gemini

1.Kubernetes

2.Terraform

3.Crossplane

4.Kubeflow

5.TensorFlow

11.Rafay

+20 more

AI Overviews

No brands listed

Multi Cloud Kubernetes Operations(1 query)

“we have clusters in AWS, Azure, and some on-prem stuff, what platforms can help me manage all of them from one place?”

0/4 platforms mentioned

ChatGPT

1.AWS

2.Azure

3.Kubernetes

4.SUSE Rancher

5.Rancher

+44 more

Claude

1.AWS

2.Azure

3.Rancher

4.SUSE

5.Red Hat Advanced Cluster Management

+9 more

Gemini

1.VMware Aria Automation

2.vRealize Automation

3.VMware

4.Red Hat Ansible Automation Platform

5.Ansible

+24 more

AI Overviews

1.AWS

2.Azure

3.Azure Arc

4.GCP

5.Google Anthos

+9 more

Platform Engineering And Self Service(1 query)

“my dev teams are complaining that getting cloud infrastructure takes too long, what software can help me build a self-service consumption engine for them?”

0/4 platforms mentioned

ChatGPT

1.Backstage

2.Humanitec

3.Terraform

4.Pulumi

5.Crossplane

+28 more

Claude

1.Terraform Cloud

2.Terraform

3.Pulumi

4.AWS Service Catalog

5.Backstage

+8 more

Gemini

1.Terraform

2.HashiCorp

3.AWS CloudFormation

4.Amazon Web Services

5.Azure Resource Manager

+15 more

AI Overviews

1.Backstage

2.Spotify

3.Port

4.Cortex

5.OpsLevel

+10 more

Evaluating Enterprise Infrastructure Platforms(1 query)

“who are the most trusted providers for enterprise Kubernetes management right now?”

1/4 platforms mentioned

ChatGPT

1.Amazon EKS

2.AWS

3.EKS Anywhere

4.EKS Distro

5.Google GKE

+34 more

Claude

1.Amazon EKS

2.AWS

3.Google GKE

4.Microsoft AKS

5.Azure

+8 more

Gemini

1.Amazon Web Services (AWS)

2.Amazon Elastic Kubernetes Service (EKS)

3.Microsoft Azure

4.Azure Kubernetes Service (AKS)

5.Azure Active Directory

+11 more

AI Overviews

1.Mordor Intelligence

2.Gartner

3.Amazon Web Services (AWS)

4.Google Kubernetes Engine (GKE)

5.GKE Autopilot

23.Rafay Systems

+18 more

Analysis

Key Insights

What AI visibility analysis reveals about this brand

Strength

Exceptional performance on Claude with a 2.0 average rank and consistent top-tier placement for 'Evaluating Enterprise Infrastructure Platforms'.

Strength

High resonance with the AI Infrastructure Architect persona, achieving a 75% mention rate, indicating strong topical authority in GPU-centric infrastructure.

Strength

Dominant mention rate on Gemini (73%), showing that Google's LLM identifies Rafay as a relevant player in the AI/ML workload management space.

Gap

Complete invisibility in 'Multi-Cloud Kubernetes Operations' and 'Platform Engineering' queries across ChatGPT and other key platforms, where competitors like Rancher and Crossplane are frequently cited.

Gap

Mixed sentiment on Gemini despite high frequency, suggesting that while the brand is known, the specific nature of the mentions may lack the authoritative backing found on Claude.

Gap

Poor ranking depth in AI Overviews (avg pos 11.4), which limits click-through potential for users seeking immediate enterprise solutions.

Opportunity

Leverage the brand's 1.0 rank on 'brand_vibe_check' to bridge the gap between AI-specific workloads and general Kubernetes management.

Opportunity

Capitalize on the existing 73% mention rate on Gemini by refining technical content to shift 'mixed' sentiment toward 'positive' through clearer ROI and security messaging.

Opportunity

Target the 'Strategic Platform Engineering Lead' persona specifically to improve the current 11.0 average position and move from a mention to a recommendation.

Technical Health

Site Health for AI Visibility

How well Rafay's website is optimized for AI agent discovery and comprehension.

95/100

19 passed 2 warnings

Audited 2/27/2026

Crawlability100

Can AI bots find your pages?

Technical90

SSL, mobile, doctype basics

On-Page SEO91

Titles, descriptions, headings

Content Quality100

Word count, depth, freshness

Schema Markup85

Structured data for AI comprehension

Social & OG95

Open Graph, Twitter cards

AI Readability100

How well AI can parse your content

Warnings

10 render-blocking resources are slowing initial render

Defer non-critical JS with async/defer. Inline critical CSS. Move stylesheets to load asynchronously.

Meta description is too short (45 characters)

Expand the description to 150-160 characters with a clear value proposition.

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Brand Identity

Brand Voice & Style

How AI perceives Rafay's communication style and personality

Rafay communicates with a confident, technically sophisticated voice that speaks directly to enterprise IT professionals. The tone balances authoritative expertise with accessibility, using industry terminology while remaining clear and solution-focused. The brand emphasizes innovation, scale, and enterprise-grade reliability, positioning itself as a trusted partner for complex infrastructure challenges.

Core Tone Traits

Technically Authoritative

Uses precise industry terminology and demonstrates deep expertise in cloud-native and AI infrastructure

Enterprise-Focused

Speaks to large-scale challenges and emphasizes reliability, compliance, and mission-critical capabilities

Innovation-Driven

Positions solutions as forward-thinking and transformative for modern infrastructure needs

Solution-Oriented

Focuses on outcomes and benefits rather than just features, addressing real enterprise pain points

Competitive Landscape

Related Ecosystem

Related products and services that AI mentions in conversations alongside or instead of Rafay

Rafay26 mentions

Terraform18 mentions

AWS18 mentions

Kubernetes15 mentions

NVIDIA GPU Operator15 mentions

Kubeflow15 mentions

Prometheus15 mentions

Crossplane14 mentions

Rancher13 mentions

VMware Tanzu13 mentions

Red Hat OpenShift13 mentions

Source Intelligence

Citations

Sources that AI assistants cite. Getting featured here improves visibility.

Top 12 Cloud GPU Providers for AI and Machine Learning in ...

https://www.runpod.io/articles/guides/top-cloud-gpu-providers

Referenced in 2 queries

Review

Unlock Massive Token Throughput with GPU Fractioning in NVIDIA ...

https://developer.nvidia.com/blog/unlock-massive-token-throughput-with-gpu-fractioning-in-nvidia-runai/

Referenced in 1 query

Review

GPU Cluster to AI Factory: 5-Stage Infrastructure Guide

https://www.vcluster.com/blog/from-gpu-cluster-to-ai-factory

Referenced in 1 query

Review

Best AI deployment platforms in 2026 | Blog - Northflank

https://northflank.com/blog/ai-deployment-platforms

Referenced in 1 query

Review

Towards Efficient GPU Cluster Management via Coordinated Multi- ...

https://ieeexplore.ieee.org/document/11245398/

Referenced in 1 query

Review

AI Infrastructure Control Plane | Optimize GPU Utilization ...

https://clear.ml/infrastructure-control-plane

Referenced in 1 query

Review

GPU Cloud Orchestration for AI Infrastructure - Rafay

https://rafay.co/solutions/gpu-cloud

Referenced in 2 queries

Review

GPU PaaS for AI Inference at the Edge - Rafay

https://rafay.co/ai-and-cloud-native-blog/gpu-paas-platform-as-a-service-for-ai-inference-at-the-edge-revolutionizing-multi-cluster-environments

Referenced in 1 query

Review

Cluster Management - NVIDIA Developer

https://developer.nvidia.com/cluster-management

Referenced in 1 query

Review

12 Best GPU cloud providers for AI/ML in 2026 | Blog - Northflank

https://northflank.com/blog/12-best-gpu-cloud-providers

Referenced in 2 queries

Review

Reduce cost and improve your AI workloads | Google Cloud Blog

https://cloud.google.com/blog/products/ai-machine-learning/reduce-cost-and-improve-your-ai-workloads/

Referenced in 1 query

Review

Top Multi-GPU Cloud Platforms for Distributed AI Training (December ...

https://www.thundercompute.com/blog/best-multi-gpu-cloud-providers-training

Referenced in 1 query

Review

Content Engineering

Goals & Content Ideas

Ideas to help AI agents better understand the business and be more likely to use Rafay's resources to help users.

Dominate Multi-Cloud Kubernetes Operations Search Visibility

Rafay is currently invisible in multi-cloud Kubernetes operations queries where competitors like Terraform and AWS capture high-intent enterprise traffic. This goal focuses on creating authoritative, AI-crawlable content that positions Rafay as the definitive solution for multi-cloud Kubernetes orchestration. Social campaigns will amplify technical content and drive engagement signals that improve AI assistant recommendations.

Why enterprises are abandoning single-cloud Kubernetes strategies in 2026

The hidden costs of managing Kubernetes across AWS, Azure, and GCP without unified orchestration

How platform teams reduce multi-cloud Kubernetes complexity by 70% with workflow automation

5 critical capabilities your multi-cloud Kubernetes platform must have for enterprise scale

Real talk: What Terraform can't solve in enterprise Kubernetes operations

Transform Gemini Documentation Sentiment to Positive

Despite strong 73% mention frequency on Gemini, mixed sentiment in technical documentation is undermining Rafay's AI visibility score. This goal prioritizes auditing and updating all technical docs, guides, and resources that Gemini references to ensure consistently positive positioning. Social channels will promote refreshed documentation and collect community feedback to reinforce quality signals.

We listened: Major updates to Rafay's technical documentation based on community feedback

Step-by-step guide to deploying GPU workloads with Rafay in under 30 minutes

Common Rafay implementation mistakes and how our updated docs help you avoid them

What enterprise architects say about Rafay's documentation after our 2026 refresh

Quick reference: Rafay CLI commands every platform engineer should bookmark

Own Platform Engineering for AI Category Leadership

Rafay excels in AI/GPU niches but lacks visibility in general Platform Engineering queries. By developing authoritative content that bridges Platform Engineering and AI infrastructure, Rafay can capture search intent across both categories. Social campaigns will establish Rafay executives as thought leaders in this emerging intersection.

Platform Engineering for AI: Why traditional approaches fail GPU-intensive workloads

The platform engineer's checklist for supporting enterprise AI initiatives

How self-service portals accelerate AI model deployment from weeks to hours

Building internal developer platforms that scale with your AI ambitions

What Netflix-scale AI infrastructure teaches us about platform engineering

Climb AI Overview Rankings with Structured How-To Content

Rafay's current AI Overview position of 11.4 misses the visibility threshold needed to capitalize on the 47% mention rate. This goal implements structured data markup and creates definitive how-to guides for self-service portal implementation that AI Overviews prioritize. Social distribution will drive engagement metrics that boost content authority.

How to build a self-service Kubernetes portal your developers will actually use

3 structured data patterns that help AI assistants understand your technical content

Step-by-step: Implementing role-based access control in self-service infrastructure portals

The anatomy of a perfect how-to guide for enterprise infrastructure automation

Why your self-service portal fails and the quick fixes that work

Content Engineering

Recommended Actions

Execute a targeted content campaign for Multi-Cloud Kubernetes Operations.

Rafay is currently 'not mentioned' in this critical query category, allowing Terraform and AWS to capture high-intent enterprise traffic.

Impact: High

Audit and update technical documentation cited by Gemini to address 'mixed' sentiment.

High mention frequency on Gemini (73%) is wasted if the sentiment is not consistently positive; this is the quickest path to increasing the overall score.

Impact: High

Develop a 'Platform Engineering for AI' authoritative whitepaper and landing page.

Rafay performs well in AI/GPU niches but fails in general Platform Engineering queries; combining these terms will help bridge the visibility gap.

Impact: Medium

Optimize for AI Overviews by implementing structured data and 'how-to' guides for self-service portals.

Current AI Overview position (11.4) is too low for visibility; moving into the top 3 will capitalize on the 47% mention rate.

Impact: Medium

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Data generated by Pendium.ai AI visibility scanning. Last scanned February 27, 2026.

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AI Visibility Score

Rafay has an AI visibility score of 39/100, rated as low. This score reflects how often and how prominently Rafay appears in responses from AI assistants like ChatGPT, Claude, and Gemini.

AI Perception Summary

Strengths

Exceptional performance on Claude with a 2.0 average rank and consistent top-tier placement for 'Evaluating Enterprise Infrastructure Platforms'.
High resonance with the AI Infrastructure Architect persona, achieving a 75% mention rate, indicating strong topical authority in GPU-centric infrastructure.
Dominant mention rate on Gemini (73%), showing that Google's LLM identifies Rafay as a relevant player in the AI/ML workload management space.

Visibility Gaps

Complete invisibility in 'Multi-Cloud Kubernetes Operations' and 'Platform Engineering' queries across ChatGPT and other key platforms, where competitors like Rancher and Crossplane are frequently cited.
Mixed sentiment on Gemini despite high frequency, suggesting that while the brand is known, the specific nature of the mentions may lack the authoritative backing found on Claude.
Poor ranking depth in AI Overviews (avg pos 11.4), which limits click-through potential for users seeking immediate enterprise solutions.

Competitors in AI Recommendations

Terraform: 18 mentions
AWS: 18 mentions
Kubernetes: 15 mentions
NVIDIA GPU Operator: 15 mentions
Kubeflow: 15 mentions
Prometheus: 15 mentions
Crossplane: 14 mentions
Rancher: 13 mentions
VMware Tanzu: 13 mentions
Red Hat OpenShift: 13 mentions
Grafana: 12 mentions
EKS: 12 mentions
GKE: 12 mentions
Azure: 12 mentions
Kubecost: 11 mentions

Categories: Enterprise Software

Tags: Startups