AI Engine Visibility Report - September 2026
We tested 202 purchase-intent prompts across ChatGPT, Claude, and Gemini to see which AI infrastructure providers appear, and how visibility changes by use case, purchase criteria, evaluation stage, and AI engine.
Models tested
3
AI engines
202
Prompts
605
Prompt-runs
107
Providers tracked
Overall AI Visibility
Which providers appear most often across the questions in this report? The leaderboard shows each provider’s overall visibility across the current selection, along with the number of prompts in which it appeared.
Top 10 providers
Visibility = share of selected prompts in which the provider is mentioned.
Visibility by Use Case
Why break visibility out by use case?
The providers that appear depend on what the infrastructure is needed for. Visibility for GPU capacity can look very different from visibility for managed inference, fine-tuning, or serverless execution.
General provider selection
Applies across all use cases when no specific use case is named.
| Buyer use case | Leading provider | Visibility |
|---|---|---|
| AI workflow infrastructure | Lambda Labs | 73.9% |
| GPU capacity | CoreWeave | 82.5% |
| General provider selection | CoreWeave | 78.3% |
| Hosted models / MaaS | Together AI | 77.5% |
| Managed inference | Together AI | 66.7% |
| Serverless execution | RunPod | 91.3% |
| Training / fine-tuning | RunPod | 69.6% |
Visibility = share of matching prompts in that use case that mention the provider. Leaders are determined across all tracked providers.
Visibility by Purchase Criteria
The providers that surface can also change depending on what matters in the decision. This view compares visibility across criteria such as cost, performance, security, availability, workload fit, and ease of use.
Capacity / availability
Cost
Ease / time-to-value
Hardware fit
Model / workload fit
Overall fit
Performance
Reliability / production readiness
Security / control
Visibility by Evaluation Stage
Visibility changes as teams move from exploring a problem to identifying solutions, discovering providers, and comparing named options. This view shows which providers appear at each stage.
Problem / job exploration
Buyer is framing the need - broad questions like “what GPU cloud should I use for training?”
Provider comparison
Buyer is evaluating named options - asking how specific providers compare.
Provider discovery
Buyer is identifying vendors - asking which providers exist for a given use case.
Solution / category discovery
Buyer is exploring approaches - understanding serverless vs. dedicated, MaaS vs. raw compute.
AI Engine Comparison
The same set of questions can produce different provider visibility across ChatGPT, Claude, and Gemini. This view compares the three engines side by side.
ChatGPT
gpt-5.6-luna
Claude
claude-sonnet-5
Gemini
gemini-3.5-flash-lite
Sources in AI Answers
AI answers draw on provider websites as well as competitors, communities, software platforms, directories, research, and other third-party sources. This section shows the mix of sources cited in the selected answers.
Citation share is the percentage of counted source citations in the selected answers that fall into each category.
| Source category | Relationship | Citation share |
|---|---|---|
| Competitor | 28% | |
| Self | 20% | |
| Adjacent | 14% | |
| Independent | 12% | |
| Adjacent | 7% | |
| Independent | 7% | |
| Independent | 4% | |
| Independent | 4% | |
| Independent | 3% | |
| Adjacent | 1% |
About This Report
- Why does AI visibility matter for infrastructure providers?
- B2B infrastructure buyers increasingly start their research with AI. Instead of searching a directory or calling a trusted contact, they open ChatGPT or Claude and ask which GPU cloud to use for training, which inference platform handles their model size, or which provider fits their security requirements. If you are not mentioned, you are not on the shortlist - regardless of how good your product is.
- What exactly is "AI visibility"?
- Visibility is the share of relevant buyer prompts in which a provider is named. A provider with 40% visibility appears in 40 out of every 100 prompts buyers ask in that category. It is a presence metric, not a sentiment or quality score. A provider with 5% visibility is largely absent from AI-mediated buyer research.
- How is visibility different from a citation count?
- A provider can be cited many times in a single AI response and still have low visibility if it appears in only a few prompts. Visibility counts the share of unique prompts in which the provider is mentioned at least once. Citation count inflates with response length; visibility reflects actual coverage across buyer questions.
- Why do visibility scores differ across ChatGPT, Claude, and Gemini?
- Each AI engine has different training data, knowledge cutoffs, retrieval mechanisms, and weighting of sources. A provider that dominates ChatGPT responses may score much lower in Claude or Gemini. Consistent visibility across all three engines is the stronger signal - it indicates the provider's positioning is well-established across independent knowledge bases.
- What buyer use cases does this report cover?
- The report covers seven infrastructure buying needs: GPU capacity, training and fine-tuning, managed inference, serverless execution, hosted models and MaaS, AI workflow infrastructure, and general provider selection. Each use case reflects a distinct buyer intent, and visibility leaders vary significantly between them.
- What does it mean if my provider has low visibility?
- AI engines are not including you in buyer research conversations for those prompts. Buyers asking which GPU cloud to use for training are not seeing your name - even if you are a strong option technically. Low visibility at the discovery stage typically means the buyer never reaches the evaluation stage with you in the comparison set.
- Can AI visibility be improved?
- Yes. Visibility improves when you know which buyer questions matter, where your brand is absent, and which sources need to support your positioning. The signals AI engines use include third-party content, community discussions, independent directories, and technical documentation. A high citation count from your own website alone is typically not enough.
- How is visibility calculated in this report?
- Visibility = the share of applicable prompts in which the provider is mentioned at least once in any AI response. For example, if a provider appears in 82 of 202 prompts, its visibility is 40.6%. Each prompt is counted once per provider, regardless of how many times the provider is mentioned within that prompt's response.
- Why do some well-known providers have lower visibility than expected?
- Visibility is prompt-specific. A provider may be well-known in the industry but not appear in AI answers for certain buyer use cases or journey stages. Common causes include: limited third-party coverage for specific use cases, absence from community discussions in that space, or positioning that AI engines do not associate with particular buyer needs.
- How often is this report updated?
- This is a point-in-time snapshot from September 2026. AI engine responses change as training data is updated, new models are released, and the information landscape shifts. Value AI Labs publishes updated editions as new data becomes available. Visibility rankings can shift meaningfully between editions.
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