Best LLM Visibility Tool: 2026 Comparison Guide

What is the best LLM visibility tool?

The best LLM visibility tool shows which buyer questions miss your brand, lets you inspect the recorded answers and helps you decide what to change. Start with Contxt if your priority is turning buyer-stage gaps and recorded answer evidence into content briefs. Consider OtterlyAI for a lower-cost monitoring baseline, Peec AI for recurring competitor analytics, or Profound for an enterprise programme. Semrush and Ahrefs are worth evaluating if you want AI visibility alongside an existing SEO research workflow.

This guide compares eight LLM visibility tracking tools by their published features, pricing and intended workflow. It covers brand visibility in AI answers—not developer observability tools for tracing LLM calls, debugging prompts or measuring model latency.

Reviewed 9 October 2026. Contxt publishes this guide and is one of the products compared. Sources are official vendor pages, not a controlled hands-on benchmark. Read our comparison method.

Best LLM visibility tracking tools compared: features, pricing and use cases

This is a shortlist by use case, not a universal first-to-last ranking. Prices below are the published starting options checked on 9 October 2026, before any applicable taxes. Annual billing, extra models, prompt allowances and enterprise terms can change the total cost. Follow the source links to confirm your requirements before buying.

Table sources: Contxt's workflow, OtterlyAI pricing, Peec AI pricing, Profound pricing, Semrush pricing, Ahrefs Brand Radar, Scrunch pricing and AthenaHQ's product page.

Which option fits your team?

• A small content team: evaluate Contxt if you need an evidence-to-brief workflow. Start with OtterlyAI if a small, recurring monitoring sample is the immediate priority.
• A reporting-led marketing team: compare Peec AI and Ahrefs Brand Radar, but distinguish tracking your exact questions from researching a wider query dataset.
• An agency with several clients: make brand workspaces, client-specific prompt sets, exports and the cost per account your first checks. A plan that suits one brand may not suit ten.
• An enterprise buying team: include Profound, then compare access controls, procurement requirements, data history and integrations—not just the number of models.
• A team focused on website readiness: examine Scrunch's site diagnostics alongside its answer monitoring. Technical accessibility and being recommended are related, but different, jobs.

Which AI visibility gaps should your tool help you close?

A visibility score is a starting point, not a content plan. Ask each shortlisted tool to show a relevant question where your brand is missing, the recorded answer, the competitors named and the pages cited. Then check whether that evidence supports one of these actions:

• Buyer-question gap: competitors appear for a relevant question and you do not. Check whether your website answers it clearly. Improve the existing page or brief a new one if the topic genuinely belongs in your offer.
• Buying-stage gap: the overall score looks healthy, but awareness, comparison or decision questions are weak. Prioritise the stage and questions that matter to your customers rather than publishing more content indiscriminately.
• Source gap: the answer cites a competitor's page or a third-party guide. Inspect what it contributes—specifications, comparisons, examples or evidence—and identify what your own content lacks. An independent review may call for legitimate outreach, not another page on your own site.
• Platform or market gap: your brand appears in one assistant or location but not another. Separate those readings and inspect their sources before treating a blended average as a universal result.

The closing workflow: identify the missed question → verify the answer and sources → prioritise a relevant gap → write an evidence-backed brief → publish or improve the page → repeat comparable checks. A brief should name the question, the existing page if there is one, the information to add, its supporting evidence and who owns the change.

Not every absence is a content problem: the question may be irrelevant to your offer, the brand may be cited without being recommended, or collection may have failed. Keep valid zero mentions separate from missing data. Consider a gap addressed only when later comparable answers support that conclusion; publishing a page alone does not establish improved visibility.

Do you need an AI visibility checker, a tracker or an API?

These solve different problems. Decide which job you need before comparing prices:

• A checker gives you a snapshot. Useful for an initial assessment or a quick demonstration. Check whether it saves answers and dates; a score alone cannot show what changed after you published new content.
• A tracker keeps comparable readings. Useful for monitoring a defined prompt set and explaining changes over time. Verify whether checks are scheduled or run on demand, how much history is retained, and whether earlier answers remain available.
• A data API supports your own reporting. Useful if you have the engineering capacity to collect, store and analyse answers. Distinguish an API that retrieves existing dashboard results from one that accepts new prompts and returns answers. A CSV export is useful, but is not the same as either API.

For a content team, a managed dashboard is usually the simpler starting point. For an engineering team, compare the total cost of collection, citation parsing, storage, retries and maintenance—not just the price per request. This article's eight-product shortlist focuses on brand-monitoring platforms rather than ranking raw-data APIs.

What does an LLM visibility tool actually measure?

An LLM visibility tool records how AI assistants answer questions about your market. It can identify whether your brand is mentioned, which competitors appear, the position of a named brand and the pages cited in the answer. You may also see these products described as AI visibility tools, AI search monitoring platforms, or AEO and GEO tools.

Monitoring tells you what happened in a sample; optimisation is the work you do in response. If the terminology is new, our explanation of how GEO differs from traditional SEO sets out that distinction.

Not all metrics mean the same thing. Before comparing dashboards, separate these measurements:

• Brand mention rate: the proportion of tested answers that name your brand.
• Recorded position: where your brand appears when the answer supplies an ordered recommendation. Not every answer includes a meaningful rank.
• Citation coverage: which URLs or domains the recorded answers reference. A citation is not automatically a recommendation of your business.
• Share of voice: your share of a defined comparison set. Ask whether the denominator is answers, brand mentions or another weighted score. It can change when competitors change, even if your own mentions stay flat.
• Sentiment: how some tools classify the language around a brand. Review the underlying passage; a positive mention is not necessarily a buying recommendation, and an automated label can miss context.
• AI referral traffic: visits arriving from AI platforms, measured separately in analytics. Mention rate does not measure traffic, leads or revenue.

For example, if 50 prompts are tested across four platforms, that produces 200 answers. If 58 of those answers mention your brand, your mention rate is 29% for that sample. It is not a claim that 29% of all AI users see your brand. Several brands can appear in the same answer, so competing mention rates need not add up to 100%.

A useful tool lets you move from that percentage to the actual prompt, answer, platform and cited source. Learn more about Contxt's AI visibility features and how a prompt becomes a content brief.

How LLM visibility tracking tools collect their data

Two dashboards can report different numbers without either being broken. They may be asking different questions or collecting a different type of answer. Ask a vendor to explain these three distinctions:

1. Research index versus your prompt set. A broad query index helps discover where a brand appears across a category. Custom tracking answers a narrower question: do you appear for the questions you chose? Neither is a census of every conversation people have with AI.
2. Consumer interface versus model API. A consumer assistant and a model API can differ in search access, citations, settings and personalisation. Ask which surface is sampled, whether web search is enabled, and whether the saved evidence includes the response and source URLs—not just a parsed score.
3. One answer versus repeated samples. Responses can vary between runs. Find out how many answers contribute to each reported figure and how the tool handles failed checks, missing answers, brand aliases and duplicate citations. A failed request should not silently become a valid “brand not mentioned” result.

Keep the prompt wording, market, platform and collection method consistent when comparing readings. Record any changes alongside the dates. An apparent visibility increase after changing the prompt library does not demonstrate that your content improved performance.

How we compared these LLM visibility tools

Each platform in this shortlist publishes a product for monitoring brand presence in AI answers. We checked official product and pricing pages for four buying questions: what the tool measures, whether it supports your own prompts, how the findings can be used, and which limits affect the quoted price.

Prices are compared on monthly billing where a monthly starting option was clearly available. Trials, custom enterprise quotes and broad query-index products are labelled separately rather than treated as equivalent subscriptions. Where we could not establish a comparable entry price, the table points to current plans instead of guessing.

The limit of this comparison: vendor documentation can establish what is advertised, but it cannot establish which product records answers most accurately, provides the best support or generates the most revenue. We have not assigned numerical scores or claimed a performance winner. Use the evaluation framework below to check the shortlisted tools against your own questions.

Eight LLM visibility tools to shortlist

Contxt: best fit for turning visibility gaps into content briefs

Contxt connects AI visibility measurement with a practical content workflow. You describe your brand, website, competitors and countries, then organise buyer questions into Awareness, Consideration and Decision stages. The same prompt set is tested across ChatGPT, Google AI Overview, Perplexity and Claude.

The value is the connection between the report and its evidence: review which questions name you, see who appears beside you, inspect recorded answers and sources, and use missed questions to inform content briefs. Buying-stage context helps distinguish an awareness gap from a comparison or decision-stage gap rather than relying on one blended score.

• Strength: a joined-up workflow from buyer prompts to answer evidence, competitor comparison and content planning.
• Reporting: visibility by platform, buying-stage context and PDF or HTML reports for sharing findings.
• Check before choosing: the scan availability and limits shown in your account. Do not assume a free account means unlimited scans.
• Best for: marketing and content teams that want to understand what the numbers mean and decide what to publish or improve next.

Create a free Contxt account to set up your brand and prompt set, or talk to the team about your requirements. Contxt measures recorded visibility; it does not guarantee recommendations, rankings or sales.

OtterlyAI: best fit for a lower-cost monitoring starting point

OtterlyAI's pricing page describes recurring prompt tracking, a Brand Visibility Index, link-citation analysis, reports and exports. Its Lite plan lists 15 prompts at $29 per month on monthly billing. Higher plans increase prompt allowances and add capabilities such as API access.

The practical constraint is the prompt allowance. Before choosing Lite, count the questions you need across products and buying stages, then check which engines and exports are included. A low entry price is useful only if the sample covers your business.

Source: OtterlyAI's official plans and pricing.

Peec AI: best fit for recurring visibility and competitor analytics

Peec AI's official plan comparison lists daily prompt tracking, visibility and position metrics, competitor analysis, source analytics and reporting features. Its Starter plan lists 50 prompts at $95 per month on monthly billing, with three selected models, one project and unlimited users. Larger plans increase allowances and reporting depth.

Peec is a relevant option for teams that want to monitor a stable prompt set regularly and compare performance across brands, models, topics or markets. Evaluate whether the source analysis and reporting in your intended plan answer your stakeholders' questions, and whether your agency or multi-project requirements need a different package.

Ask to see how a weekly report separates platform changes from changes to the tracked questions. Also check project limits and plan-specific exports if the dashboard needs to support several teams or clients.

Source: Peec AI's official pricing and feature comparison.

Profound: best fit for enterprise AI-search operations

Profound's current pricing page distinguishes a free trial from custom enterprise packages. The trial runs 50 prompts daily for seven days, with a recommended prompt set and restricted functionality. Enterprise packages offer tailored tracking, broader engine coverage and marketing-agent workflows.

For larger organisations, the procurement questions extend beyond a visibility score: ask about governance, access controls, security requirements, data access and how insights move into recurring marketing work. Profound's enterprise proposition is relevant to that conversation, but the trial should not be treated as a like-for-like representation of an ongoing enterprise subscription.

The published trial is limited: its recommended prompts are not customisable, and its feature table lists no history, exports or API access. If those are central to your evaluation, ask for an enterprise demonstration using your requirements rather than judging the product solely on the trial.

Source: Profound's official trial and enterprise comparison.

Semrush: best fit for combining AI visibility with SEO research

Semrush's AI visibility feature page describes brand mentions, share of voice, sentiment, prompt research and competitor analysis. Its broader platform also supports established SEO workflows, making it worth considering if your team already uses Semrush to research and plan search activity.

For an existing Semrush customer, the decision is whether the included AI data answers the question you are trying to solve. Distinguish broad market research from tracking your exact buyer questions. Ask to see your custom prompt allowance, platform coverage and the package needed before assuming the SEO subscription already covers the work.

Sources: Semrush's AI visibility features and current pricing.

Ahrefs Brand Radar: best fit for broad research and custom prompt checks

Ahrefs separates two approaches: an AI Visibility Index for researching brands across a large query dataset, and Custom Prompts for tracking specific buyer questions. Its product page lists custom prompt options from $50 per month and AI Visibility Index options from $199 per month, alongside allowances included in eligible paid Ahrefs plans.

That distinction matters for smaller or specialist brands. Broad index research helps explore a category; a carefully chosen custom prompt set answers whether your business appears for the questions that actually matter to you. These are complementary jobs, not interchangeable measures of performance.

Budget for the research index and custom checks separately. Check how selected platforms, locations and update frequency consume your allowance; a headline count of prompts does not describe the full workload.

Source: Ahrefs Brand Radar's product and pricing page.

Scrunch: best fit for citation monitoring and AI-focused site diagnostics

Scrunch's product pages describe brand-presence monitoring, citation-source analysis and site diagnostics. Its Core plan lists $250 per month, 125 unique prompts, 5,000 monthly responses, five site audits and one brand workspace.

It is worth evaluating if you want to connect what AI says about your business with how AI systems encounter your website. Citation types and source movement can help distinguish your own pages from competitor or third-party references. Site diagnostics add a separate technical layer to the monitoring discussion.

Its 125 unique prompts do not mean unlimited answer collection. Ask how the 5,000-response budget is distributed across models and runs, and what the five monthly audits cover. Those limits matter if you plan frequent checks or need to inspect a large site.

Sources: Scrunch's monitoring and citations page and official pricing.

AthenaHQ: best fit for a cross-functional GEO workflow

AthenaHQ describes cross-platform visibility tracking, citation-source analysis, content optimisation recommendations and executive reporting. Its positioning spans SEO, content, PR and brand teams, rather than treating monitoring as an isolated reporting task.

In the demo, follow one missed question all the way from its recorded answer to an assigned action and a later measurement. That is a more useful test of the workflow than the size of a model list. Confirm pricing and usage allowances for the package that demonstrates it.

Source: AthenaHQ's official product page.

Worked example: what a useful AI visibility report should show

Illustrative data—not a live client result or a benchmark of the tools above. Imagine a power-tool brand testing 50 buyer prompts across four platforms. Ten prompts cover Awareness, 30 cover Consideration and ten cover Decision, producing 200 answers.

The actionable finding is not simply “visibility is 29%”. The brand appears in decision answers but misses the tested awareness questions. Open those questions, inspect who is named and which pages are cited, then decide whether an existing page needs improving or a new brief is justified.

For a question such as “What should I look for in a cordless drill?”, the report should preserve the answer and its sources. A brief should specify the buyer question, the useful information missing from your page, the supporting evidence and the next measurement. Re-test comparable prompts after publishing; a single improved answer does not prove that the change caused it.

This is the evidence-to-action workflow to ask for in a demo: see how Contxt connects prompts, reports and content briefs.

How to choose the best LLM visibility tool for your business

Use these six checks before committing to a subscription:

A prompt library is not just a keyword list rewritten as questions. Our guide to buyer prompts versus SEO keywords explains why the wording and intent of the question matter. Include decision-stage questions as well as broad category discovery; otherwise a healthy average can hide gaps near a buying decision.

1. Relevant platform coverage. Start with the AI assistants your buyers use. Verify each platform is included in your intended plan, and distinguish Google AI Overview from other Google search experiences.
2. Buyer-intent prompts. Include awareness, comparison and decision questions. Check whether you can edit prompts, add competitors and control markets or languages where needed.
3. Answer-level evidence. Ask to see the recorded response behind a score, its date and its cited URLs. A percentage without evidence is difficult to explain or act on.
4. Comparable repeat readings. Find out how sampling works, how often checks run and whether you can compare the same questions over time. Model and prompt changes can affect the result.
5. A usable next action. Ask the vendor to demonstrate the gap-closing workflow on one relevant question: missing brand, competitor and source evidence, a prioritised content change and a later comparable reading. Find out which steps the tool supports and which your team must do manually.
6. The total cost of your sample. Compare prompts, platforms, markets, frequency, history and users together. Ask about add-ons and usage limits, not just the headline monthly price.

A useful planning equation is: prompts × platforms × checks per month × markets. For example, 50 prompts across four platforms, checked weekly in one market, produce roughly 800 answer checks in a four-week month. This is a workload illustration, not a claim that every vendor bills by that formula or that Contxt automatically runs weekly scans.

A practical way to evaluate your shortlist

Use a fixed prompt set to compare tools fairly. This is a suggested evaluation process, not a claim that we conducted a controlled trial across these products.

Download the LLM visibility evaluation worksheet (CSV). It includes three example buyer prompts across four platforms, with empty fields for dates, markets, brand mentions, positions, sources and answer evidence. Replace the example questions with your own and duplicate the rows for each tool and reading. The empty fields are deliberate: this is a collection template, not fabricated test data.

1. Define the business question. For example: “Are we missing from the comparison questions buyers ask before choosing a supplier?”
2. Select a manageable prompt set. Include non-branded questions at each buying stage and a consistent group of competitors. Avoid filling the sample with prompts that already name your business.
3. Inspect the baseline. Review named brands, citations and any recorded ranking. Check a few underlying responses rather than trusting the headline score alone.
4. Choose one evidence-backed content change. Improve a page that answers a missed question, or develop a brief around a genuine gap. Do not treat a third-party citation count as a guaranteed route to a recommendation.
5. Repeat the same questions. Compare equivalent readings after the change. Record the dates and any model, prompt or sampling differences; one improved result does not establish causation.

If your priority is turning those findings into a content plan, see Contxt's question-to-brief workflow. For hands-on help with the strategy, talk to our GEO services team.

Best LLM visibility tool: frequently asked questions

What is the difference between an LLM visibility tool and an SEO tool?

An LLM visibility tool studies recorded AI answers: brand mentions, competitors, positions and citations. Traditional SEO tools primarily study search rankings, keywords, links and website performance. Some platforms now combine both, but appearing in a search result and being named in an AI recommendation are different outcomes.

Which LLM visibility tool is best for a small marketing team?

Choose according to your next task. Contxt is worth evaluating for buyer-stage analysis and content briefs. OtterlyAI has a lower published starting subscription price for a small prompt set. Check coverage and allowances before deciding; the cheapest plan is not automatically the best fit for your category.

Can I track AI visibility for free?

Yes. For a small baseline, run the same non-branded questions manually, save the answers and cited URLs, and record the platform and date in the evaluation worksheet. The limitation is the work of repeating and preserving those readings. You can also create a free Contxt account to set up your brand and prompts; scan availability and limits are shown after sign-in. Some vendors offer trials or limited checks, but free access should not be confused with unlimited recurring monitoring.

What is the difference between an AI visibility checker and a tracker?

A checker provides a reading at one point in time. A tracker preserves repeated readings so you can inspect changes in brand mentions, citations and competitors. Check the actual features rather than relying on the product name: saved answers, historical access and consistent sampling are what make a trend useful.

Can I send LLM visibility data to my own reporting system?

Some platforms provide exports or APIs, but access and allowances vary by plan. Ask whether you can retrieve the recorded answers, prompt identifiers, timestamps and citation URLs—not just an aggregate visibility score. An API for stored dashboard results is different from a service that collects new AI answers on request.

Does ChatGPT visibility represent every AI platform?

No. ChatGPT, Google AI Overview, Perplexity and Claude can produce different answers to the same question. Compare platform-specific results rather than assuming one reading describes the whole market. Our article on differences between ChatGPT and Google AI visibility explores this in more detail. Also check how each vendor collects its sample; an API response may differ from the consumer product.

Can an AI visibility tool guarantee that my brand will rank?

No. A tool can measure a sample, reveal gaps and help prioritise improvements. It cannot control an AI assistant's recommendations. Answers can vary with wording, location, product settings and time. Evaluate repeatable evidence, not promises of guaranteed positions or sales.

How often should I check LLM visibility?

Choose a cadence that matches your decisions and budget. Regular checks can reveal changes, but frequent sampling is not automatically more actionable. Keep a stable prompt set for comparison, and record content changes and relevant platform updates so you can interpret the trend.

Choose a tool that helps you act, not just watch

The best LLM visibility tool for your team connects a meaningful buyer question to evidence you can explain and an action you can take. Compare coverage, source visibility, prompt control and total cost before choosing. Then judge progress using equivalent repeat readings rather than one headline score.

Want to see where AI names your brand? Create a free Contxt account, add your market and buyer questions, and review the scan options available to you.