AI Visibility
measured66/100
Frequently Cited · in GPU Cloud
counts brand mentions in AI answers, not source citations
AEO Readiness
fundamentals26/100
Weak fundamentals
Avg Prompt Score
45
across 461 prompts
AI Share of Voice
45%
across 461 prompts
Critical Issues
5
critical + high
Shortlist Position
33/65
Challenger · Gpu Cloud Discovery
How AI Visibility breaks down
in GPU Cloud
Composite = 60% coverage + 40% engine breadth, measured in the brand's strongest category. Position and citation depth join the score once the aggregation pipeline captures them. This is the measured Visibility score — separate from AEO Readiness, which scores fundamentals.
Per-stage performance
Cited rate · share of voice · engine consensus · sentiment, broken out by buyer-journey stage. Sentiment is the net positive−negative skew across engines that cited the brand at this stage.
Categories RunPod is visible in
2Your visibility gap
RunPod surfaces in 33/65 Gpu Cloud discovery prompts. Claim this profile to see which 32 prompts you're missing and how to win them.
Executive summary
Runpod is already cited for core GPU-cloud discovery queries (A100/H100 availability, 80GB VRAM, and “cheapest GPU cloud for AI startups”), and it has crawlable, specific pricing on first-party pages. The biggest AI-citation risk is evaluation-stage coverage: missing/unclear first-party answers for Kubernetes and fast networking between nodes, plus likely gaps in SoftwareApplication/FAQPage structured data that help models answer “platform/tooling” questions. Highest-ROI fix: publish and SSR-index dedicated pages that directly answer the highest-value missing buyer questions (fast networking, Kubernetes support, and cloud training performance tooling) with evaluation-style Q&A blocks and schema.
Based on audit of runpod.io · May 5, 2026