Best AI Visibility Tools (2026): 15 Compared by Job & Price

The best AI visibility tool is the one built for your job, not the one with the longest engine list. Audit once with AEOeye or a manual check, track with Otterly.AI, Peec AI or Trakkr, go enterprise with Profound, AthenaHQ or Scrunch, or stay in your SEO suite's AI add-on.

This guide is based on public documentation and pricing pages as of 3 October 2026; we have not tested any competitor's product hands-on. AEOeye is our own product, so we say where trackers beat it.

What does an AI visibility tool actually do?

An AI visibility tool, also sold as an AEO, GEO or LLM visibility tool, sends buyer-style questions to AI engines on a schedule and reports mention rate, position, share of voice against competitors and cited sources. GEO and AEO are two names for the same practice, formalised as GEO in a KDD 2024 paper.

Two different products sit under that label. Answer-based tools ask the engines and read what comes back. Crawl-based tools only check whether bots can reach your pages, which most brands already pass: in AEOeye's crawler access study (October 2026, convenience sample), 1 of 193 SaaS sites and 4 of 90 e-commerce and consumer sites blocked any AI retrieval bot in robots.txt.

The vendors pass too: none of 43 AI-visibility vendor sites (aeoeye.com included) blocks any AI bot we tested, 27 of 41 serve llms.txt and 18 of 42 homepages carry FAQPage markup in served HTML (per-site CSV). That is hygiene, not measurement quality, but check yours once: OpenAI documents that sites disallowing OAI-SearchBot will not appear in ChatGPT search answers.

How should you choose an AI visibility tool?

Choose the job first, then engines, prompt allowance and seats; compare price last. The expensive mistake is buying the wrong job: page optimizers, schema generators and crawler checks fix pages but cannot show whether engines name you.

Three checks decide most purchases:

1. Engines in the entry tier. Claude shows the gating best: Trakkr, LLMrefs and Nightwatch list it, Otterly.AI and Peec AI sell it as an add-on, Scrunch has it only on Enterprise, and Semrush does not list it.
2. Prompts × engines × runs. Price per prompt hides the multiplier. By our arithmetic from published limits, Trakkr Growth's 50 prompts on eight models, run daily, give about 12,000 answers in 30 days for $100; Peec Starter's 50 prompts on three active models give about 4,500 for $95. LLMrefs derives prompts from keywords and Rankscale meters credits, so counts differ.
3. Seats and exports. Agencies need multi-brand workspaces, white-label reports and an API; Trakkr Scale and Rankscale Growth list them.

Which tools fit which job?

Match your job to a row group, and judge entry price by prompt allowance, not the headline number. Prices are public list prices in US dollars unless marked, from vendor pages as of 3 October 2026; Peec's is from its July 2026 plan summary.

For plan detail, see our Peec AI and Otterly.AI pricing guides.

One-off audit or always-on tracking: which do you need first?

Run a one-off audit first. It shows whether any engine names you and who wins instead, and a tracker earns its monthly fee only once there is a gap you will act on weekly. Add tracking when you have a stable prompt set, several markets, frequent launches or an executive asking for trend lines.

AEOeye's free audit asks three unbranded buyer questions on ChatGPT; the one-time $29 report adds Claude, Gemini, Google AI and Perplexity and 10 questions, with no subscription. It does not track daily or draw trend lines, so trackers beat it on history, run frequency and prompt volume, and enterprise platforms on sampling depth and security review.

The workable sequence: audit, diagnose, fix the page behind each lost question, then re-ask the same questions on a schedule. You can start free by hand: put 10 to 15 buyer questions to ChatGPT, Perplexity and Gemini in fresh sessions and log who is named and cited weekly. Our free checker roundup and tracker comparison cover the tools for each step; building on model APIs saves cash but adds upkeep.

Enterprise platforms: when are they worth it?

Enterprise platforms are worth it when several teams will use the data weekly, or when you need sampling depth, security review and services a $100 plan cannot. If one marketer opens the dashboard monthly, you are paying for decoration. Evertune says it samples each prompt up to 100 times per model; Profound lists SOC 2, SSO and up to nine engines.

Test before the demo. Profound's trial runs a recommended prompt set for your industry and does not allow custom prompts, so it cannot test your buyer questions; Enterprise can. Action layers (AthenaHQ's on- and off-page actions, Scrunch's monthly site audits) are checklists until a recommendation names the specific page, source or entity to fix. See AEOeye vs Profound.

SEO suites with AI add-ons: good enough?

For teams that already own the suite, yes, with one limit: prompt allowance. These add-ons suit a small, stable question set beside your rankings. Semrush tracks 25 custom prompts per domain, SE Ranking Core 100 and Nightwatch Starter 50.

Ahrefs sells custom prompts and an index of 448 million-plus prompts that, it says, works best once your brand already appears in AI search, so small brands should use custom prompts. Confirm any add-on shows the full answer per prompt. Google says Google AI Overviews and AI Mode need no special optimization, files or schema beyond normal Search eligibility, so a suite's SEO data still matters. "AI SEO" writing assistants do not measure answers, and Google warns that mass-producing AI pages without added value may violate its scaled content abuse policy.

What should you demand from any tool?

Demand raw answers, repeat runs, your own buyer questions and a next step; everything else is a dashboard. Use this checklist on any vendor, including us; our buyer's guide adds a scorecard and a test:

1. Raw answers. Every score should trace to a prompt, engine, answer text and date. Trakkr's documentation says you can open any prompt and read the response behind a score; ask others the same.
2. Repeat runs. OpenAI documents its API as non-deterministic by default. In SparkToro's 2,961-run study (run with Gumshoe.ai, itself a tool vendor), any two responses had under a 1-in-100 chance of sharing a brand list. Track visibility percentage across many runs, not rank position; its authors agree.
3. Your buyer questions. Test each finalist with the same 10 to 20 unbranded buyer questions; brand-name prompts flatter any score.
4. Collection method. Peec simulates the ChatGPT interface and lists OpenAI's Search API as a separate model; Trakkr calls providers' APIs. Ask which surface you are paying for.
5. Separate outcomes. Mentioned, recommended and cited are different results. Insist on engine-by-engine results with competitors and sources named, not a blended score.
6. Presence before sentiment. In AEOeye's audit records (127 audits, small and self-selected; aggregates), 157 of 892 answers named the audited brand and 144 of those 157 mentions were positive. Absence, not negativity, was the usual problem.
7. A next step and an exit. Each gap should name a page, source or entity to fix, with exportable data and a trial or monthly billing before any annual lock-in.