Modular

Visibility23

Vibe95

Businesses/Artificial Intelligence Infrastructure/Modular

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AI Visibility & Sentiment

Modular

Modular is an AI infrastructure company that provides GPU portability and high-performance computing solutions for demanding AI workloads. They offer a unified platform that enables enterprises to deploy and scale AI applications across different hardware vendors with unprecedented performance and cost efficiency.

Active Monitoring

modular.com

Artificial Intelligence Infrastructure

AI Visibility Score

23/100

Low

Sentiment Score

95/100

AI Perception

Summary

Modular has successfully captured the attention of high-performance ML engineers with a 71% mention rate, yet it remains nearly invisible to the CTOs and architects who control enterprise budgets. While Claude recognizes Modular as a premium solution for specialized development tooling, the brand is being systematically excluded from critical conversations regarding GPU cost optimization and hardware portability—territories currently dominated by NVIDIA and PyTorch.

Value Proposition

GPU portability and blazing-fast AI performance with no vendor lock-in—enabling organizations to run AI workloads across NVIDIA, AMD, and other hardware with up to 70% faster performance and 80% cost savings compared to traditional solutions.

Overview

Mission

To democratize AI infrastructure by providing hardware-portable, high-performance AI solutions that free organizations from vendor lock-in while delivering unprecedented speed and cost efficiency.

Products & Services

MAX Platform - AI inference and deployment engineMojo - High-performance programming language for AIMammoth - Large-scale AI model servingGenAI model support (500+ models)Enterprise AI infrastructure solutions

Agent Breakdown

AI Platforms

How often do different AI platforms reference Modular?

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Conversation Analysis

Topics

What conversations is Modular included in — or excluded from?

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Buyer Personas

Personas

Who does each AI platform recommend Modular 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.

AI Infrastructure Performance & Cost Optimization

3 queries

“how to lower our gpu cloud costs for running llama 3 in production”

0/4

“what's the fastest inference engine for deploying large language models right now”

0/4

“help me speed up my ai inference pipeline, what specific tools should i use”

0/4

High-Performance AI Development Tooling

3 queries

“better alternatives to python for high performance ai development”

1/4

“should i use c++ or is there a newer language for ai systems programming”

1/4

“how to get better performance from ai models without writing custom cuda kernels”

0/4

Hardware Portability & Multi-Vendor Strategy

3 queries

“how to build an ai stack that isn't locked into nvidia gpus”

0/4

“set up a multi-cloud ai deployment strategy that works on both amd and nvidia hardware”

0/4

“ways to migrate ai workloads from cuda to other hardware platforms”

1/4

Enterprise AI Infrastructure Trust & Selection

1 query

“most trusted enterprise ai infrastructure companies for large scale deployment”

0/4

Analysis

Key Insights

What AI visibility analysis reveals about this brand

Strength

Exceptional resonance with the High-Performance ML Engineer persona, achieving a 71% mention rate.

Strength

Strong technical authority on Claude with an average position of 3.9 across relevant queries.

Strength

High visibility in specialized development language queries, specifically positioning as a top alternative to Python and C++ for AI systems.

Gap

Total absence in high-intent financial queries such as 'lower gpu cloud costs for running llama 3' and 'ai infrastructure cost optimization'.

Gap

Critical lack of visibility in hardware portability and multi-vendor strategy discussions, where competitors like AMD and ROCm are gaining ground.

Gap

Failure to penetrate the 'Efficiency-Focused CTO' persona, resulting in a negligible 6% mention rate.

Opportunity

Reposition Mojo and MAX from purely 'fast' tools to 'cost-saving' infrastructure to capture the high-volume cloud expenditure queries.

Opportunity

Aggressively target the 'NVIDIA lock-in' narrative to appear in hardware-agnostic deployment queries where Modular currently has zero visibility.

Opportunity

Capitalize on positive sentiment within AI Overviews by creating structured data around 'Enterprise AI Infrastructure Trust' to displace legacy incumbents.

Technical Health

Site Health for AI Visibility

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

87/100

16 passed 2 warnings 2 issues

Audited 2/24/2026

Crawlability96

Can AI bots find your pages?

Technical90

SSL, mobile, doctype basics

On-Page SEO82

Titles, descriptions, headings

Content Quality60

Word count, depth, freshness

Schema Markup85

Structured data for AI comprehension

Social & OG100

Open Graph, Twitter cards

AI Readability100

How well AI can parse your content

Critical Issues

Page has no H1 heading

Add a single H1 tag as the main page heading.

Content is too thin

Expand your content to at least 300-500 words with valuable information.

Warnings

4 render-blocking resources are slowing initial render

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

Few headings on page

Add more H2 and H3 headings to organize content into sections.

Few internal links on this page

Add more internal links to related pages on your site.

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Brand Identity

Brand Voice & Style

How AI perceives Modular's communication style and personality

Modular communicates with confident technical authority while remaining accessible to developers and enterprise decision-makers alike. The voice is bold and performance-focused, frequently using concrete metrics and benchmarks to substantiate claims. There's an underlying tone of innovation and disruption—positioning Modular as the solution to industry pain points like vendor lock-in and infrastructure complexity. The brand balances technical depth with clarity, making complex AI infrastructure concepts understandable without dumbing them down.

Core Tone Traits

Technically Authoritative

Speaks with deep expertise on AI infrastructure, using precise terminology and concrete performance metrics

Bold & Confident

Makes strong claims backed by data, positioning as an industry leader challenging the status quo

Developer-Centric

Addresses technical audiences directly with practical, hands-on language that resonates with engineers

Performance-Obsessed

Consistently emphasizes speed, efficiency, and measurable improvements in every communication

Competitive Landscape

Related Ecosystem

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

NVIDIA91 mentions

PyTorch87 mentions

CUDA86 mentions

Modular68 mentions

AMD65 mentions

TensorFlow60 mentions

TensorRT55 mentions

Intel55 mentions

ROCm54 mentions

ONNX Runtime53 mentions

vLLM44 mentions

Source Intelligence

Citations

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

Reducing GPU Costs for Production AI

https://ai-infrastructure.org/reducing-gpu-costs-for-production-ai/#:~:text=by%20Modzy%20%7C%20Jun%201%2C%202023,for%20running%20your%20AI%20applications.

Referenced in 1 query

Review

Cost comparison and basic deployment patterns - Llama

https://www.llama.com/docs/deployment/cost-comparison/#:~:text=Managed%20hosted%20APIs%20charge%20per,even%20with%20higher%20hourly%20rates.

Referenced in 1 query

Review

Autoscaling Llama Server in the Cloud from $0.08/hr - Medium

https://medium.com/terasky/autoscaling-llama-server-in-the-cloud-from-0-08-hr-92917f906199#:~:text=Spot%20instances%20are%20ideal.,spot%20prices%20cut%20in%20half.

Referenced in 1 query

Review

Cost of self hosting Llama-3 8B-Instruct - Hacker News

https://news.ycombinator.com/item?id=40681784#:~:text=Instead%20of%20using%20AWS%20another,%7C%20parent%20%7C%20next%20%5B%E2%80%93%5D

Referenced in 1 query

Review

SkyServe: Serving AI Models across Regions and Clouds with ...

https://arxiv.org/html/2411.01438v2#:~:text=Spot%20instances%20have%20long%20been%20offered%20by%20cloud%20providers%20as,the%20same%20zone%20or%20region.

Referenced in 1 query

Review

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

https://www.runpod.io/articles/guides/top-cloud-gpu-providers#:~:text=Rapid%20prototyping%20and%20experimentation%20without,deep%20learning%20projects%20using%20Runpod.

Referenced in 1 query

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/#:~:text=LLM%20inference%20enterprise%20challenges,can%20consume%20the%20same%20GPUs.

Referenced in 1 query

Review

Deploying LLaMA 3 8B Instruct on OKE with NVIDIA NIM ...

https://www.youtube.com/watch?v=Od6xlWQH0d4&t=3

Referenced in 1 query

Pitch Story

How Much Do GPU Cloud Platforms Cost for AI Startups in 2025?

https://www.gmicloud.ai/blog/how-much-do-gpu-cloud-platforms-cost-for-ai-startups-in-2025#:~:text=Maximize%20utilization.,so%20interrupted%20work%20resumes%20seamlessly.

Referenced in 1 query

Review

Meta's Llama - Models in Amazon Bedrock - AWS

https://aws.amazon.com/bedrock/meta/#:~:text=Since%20Amazon%20Bedrock%20is%20serverless,best%E2%80%94building%20your%20AI%20applications.

Referenced in 1 query

Partner

Best Cloud Providers for Budget AI Deployments - Latitude

https://latitude.so/blog/best-cloud-providers-for-budget-ai-deployments#:~:text=AWS:%20Offers%20a%20wide%20range,fewer%20general%2Dpurpose%20compute%20options.

Referenced in 1 query

Review

5 Cheapest Cloud Platforms for Fine-tuning LLMs - KDnuggets

https://www.kdnuggets.com/5-cheapest-cloud-platforms-for-fine-tuning-llms#:~:text=When%20it%20comes%20to%20fine,documentation%20designed%20for%20data%20professionals.

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 Modular's resources to help users.

Dominate GPU Cost Efficiency Search Visibility

Address the critical gap where Modular is absent from AI responses about lowering cloud costs and LLM deployment. Launch a comprehensive content blitz targeting 'GPU cost efficiency,' 'Llama 3 deployment costs,' and 'LLM infrastructure ROI' keywords to establish Modular as the go-to solution for cost-conscious AI teams. Amplify this content through social media with concrete benchmark data and cost comparison graphics that AI systems can reference.

How We Cut Llama 3 Deployment Costs by 80% Without Sacrificing Performance

The Hidden GPU Costs Killing Your AI Budget—And How to Fix Them

Real Numbers: Running LLMs on Modular vs Traditional Cloud Infrastructure

Why Your CFO Should Care About GPU Portability

5 Cost Metrics Every AI Team Should Track Before Their Next Hardware Decision

Own the Hardware Portability Narrative

Combat NVIDIA and CUDA's monopoly on infrastructure conversations by developing authoritative content around 'multi-vendor AI stacks' and 'moving beyond CUDA.' Create and index technical whitepapers, blog posts, and social content that positions Modular as the definitive voice on hardware flexibility. Share technical deep-dives and migration success stories across developer communities and LinkedIn to build indexable authority.

Breaking Free from CUDA: A Technical Guide to Hardware-Agnostic AI

Multi-Vendor AI Stacks: What Enterprise Teams Need to Know in 2026

The Real Cost of GPU Vendor Lock-In—A Technical Analysis

AMD vs NVIDIA vs Both: Why Modern AI Teams Choose Portability

How One Enterprise Migrated 50 AI Workloads Across Hardware in 30 Days

Elevate Executive AI Infrastructure Awareness

Address the alarming 6% CTO mention rate by launching targeted executive-focused content that translates technical GPU portability benefits into business outcomes. Create C-suite accessible content around AI infrastructure ROI, total cost of ownership, and strategic flexibility that resonates with decision-makers beyond the engineering layer. Leverage LinkedIn thought leadership and industry publications to reach enterprise buyers.

The Executive's Guide to AI Infrastructure: Beyond the Engineering Hype

Why Your AI Strategy Needs Hardware Flexibility—A Business Case

3 Questions Every CTO Should Ask About Their AI Infrastructure Costs

From Vendor Lock-In to Strategic Advantage: An AI Infrastructure Playbook

What Fortune 500 CTOs Get Wrong About AI Deployment Economics

Optimize Cross-Platform AI Visibility Parity

Close the significant visibility gap between AI platforms (17% ChatGPT vs 27% Claude) by optimizing technical documentation and content structure for uniform LLM ingestion. Restructure existing technical docs with clearer formatting, consistent terminology, and enhanced metadata that ChatGPT and Gemini can better parse. Syndicate optimized content across multiple high-authority platforms to increase crawlability.

Modular Technical Documentation: Quick Start Guide for Enterprise Deployment

GPU Portability FAQ: Everything You Need to Know About Hardware Flexibility

Benchmarks Deep Dive: Modular Performance Across NVIDIA, AMD, and Intel

Step-by-Step: Deploying Your First AI Workload on Modular's Platform

Modular Architecture Explained: How We Achieve 70% Faster Performance

Content Engineering

Recommended Actions

Execute a 'GPU Cost Efficiency' content blitz targeting Llama 3 and LLM deployment keywords.

Modular was not mentioned in any queries related to lowering cloud costs, representing a massive missed opportunity to connect technical performance to business ROI.

Impact: High

Develop and index technical whitepapers specifically addressing 'multi-vendor AI stacks' and 'moving beyond CUDA'.

Modular is currently invisible in hardware portability searches, allowing NVIDIA and CUDA to maintain a monopoly on the infrastructure narrative.

Impact: High

Launch an executive-focused 'AI Infrastructure ROI' campaign to increase the 6% CTO mention rate.

Decision-makers are currently unaware of Modular; visibility is trapped in the engineering layer, which hinders enterprise-wide adoption.

Impact: Medium

Optimize technical documentation for ChatGPT and Gemini to mirror the high performance seen on Claude.

Significant visibility disparities between platforms (17% on ChatGPT vs 27% on Claude) suggest that Modular's current data footprint is not being uniformly ingested by all major LLMs.

Impact: Medium

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General Catalyst

Data generated by Pendium.ai AI visibility scanning. Last scanned February 24, 2026.

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