AI visibility software ranked: 10 tools compared on real pricing, engine coverage, and which vendors' citation methodology actually holds up under audit.
Every AI visibility vendor will hand you a number: a visibility score, a share of voice, a percentage of prompts where you got named. What almost none of them will hand you, unprompted, is the math behind it. That gap is the whole reason a buyer can't just sort this category by "highest score" and call it done. This post ranks the full field, ten tools from free to enterprise, on real, current pricing and methodology transparency, then gives every entry a report card. If you've already narrowed the field to the three most-searched names, our head-to-head on Profound, Otterly, and Scrunch AI by team size goes deeper on those three alone; this post is the wider map.
Evertune holds up best under outside audit: it's the only tool on this list that publishes its own sampling margin of error, and that rigor costs $800 or more a month. The rest of the field trades some of that transparency for lower prices and broader reach.
| Tool | Entry price/mo | Engines tracked | Publishes accuracy evidence? |
|---|---|---|---|
| Evertune | ~$800, demo-led | Custom | Yes, margin of error |
| Profound | Enterprise, custom | Broadest, Gartner-named | No |
| AthenaHQ | $295 | 8 | No |
| Otterly | $29 | 4 (add-ons for more) | No |
| Rankscale | $99 | 8 | No |
| HubSpot AI Search Grader | Free | 3 | No |
Only 6 of the 74 tools in CitedIndex's August 2026 audit publish evidence like that. None of the 10 below write the page that closes a citation gap, which is the job Lyra's pipeline does instead.
An AI visibility score looks like a single, comparable number. It isn't. It's the output of a pipeline with at least three variable stages, sampling method, denominator choice, and citation definition, and a vendor can change any one of them without changing the label on the dashboard.
Vendors in this category build their numbers three different ways. Crawl-based tools scrape AI-generated search surfaces, like Google's AI Overviews, and count when a domain shows up in the rendered result. Sampled citation trackers run a fixed panel of prompts directly against model APIs on a schedule and log the responses. Self-reported share of voice tools ask a model to estimate a brand's standing relative to competitors in a single pass, which is closer to asking the model for its opinion than measuring its behavior.
These aren't interchangeable. A crawl-based number reflects what a live search surface rendered at one moment. A sampled number reflects a probability estimate built from repeated queries. Confusing the two is how a team ends up comparing a weather report to a climate model and treating the mismatch as a real change in the weather.
Here's the part that catches most buyers off guard: two vendors can run the identical brand through their pipelines on the identical day and land on different scores, and both can be defensible. The reason is the denominator.
Say a brand gets mentioned in 50 out of 100 total AI responses to a prompt panel. One vendor divides by all 100 responses and reports 50%. Another divides only by the responses that mentioned any brand in the category at all, and if that's 60 of the 100 responses, the same 50 mentions become 50/60, or 83.3%. "Nothing about the brand changed. The denominator removed 40 failures," as an independent methodology audit at Authoritytech.io put it. Neither number is fabricated. They just answer different questions, and a dashboard rarely tells you which one it's showing you.
Sample size compounds the problem. Evertune's own disclosed testing found that asking 100 distinct prompts once each produced a margin of error of roughly plus or minus 9 points, while repeating each prompt 100 times narrowed that to about plus or minus 1 point overall, according to CitedIndex's methodology census. Profound, by contrast, runs prompts once per day per configured model rather than repeating them dozens of times in a single pass, per the Authoritytech.io audit. That's not a flaw in either company's product. It means the two vendors are estimating fundamentally different probability distributions from the same brand, and stacking their scores side by side in a spreadsheet produces a comparison that was never valid to begin with.
The prompt itself can distort the number too. Naming a brand directly inside a measurement prompt, "how does Acme compare to competitors," has been shown to inflate that brand's apparent recommendation rate by more than 20 percentage points compared to a category-only prompt like "what's the best tool for X," per the CitedIndex census. A vendor that lets you write your own prompts can hand you a flattering number without meaning to.
None of this is trivial vendor-shopping detail. It's the reason the CitedIndex census, which reviewed 74 AI-visibility tools as of August 16, 2026, found that only 6 tools, 8% of the field, published inspectable supporting material for their accuracy claims. Thirty-six tools, 49%, made accuracy claims with no published support behind them, and 32, 43%, made no accuracy claim at all. "A specific number is not validation by itself," the census concluded. Keep that line in mind for every ranking below.
This list orders the field by a mix of methodology transparency, engine coverage, and price, in that priority: a vendor that shows its work but costs more earns a higher spot than a cheaper one that won't say how it samples. Every price below is current as of this post's date; check each vendor's live pricing page before budgeting, since these tools revise plans often.
Evertune samples each prompt up to 100 times per model rather than once, and publishes the resulting margin of error, the kind of disclosure the CitedIndex census found in only 8% of the category. That transparency is the whole case for Evertune: you can audit what the score actually represents instead of taking it on faith. Evertune was also named a Representative Vendor in the 2026 Gartner Market Guide for Answer Engine Visibility Tools, alongside Profound and Semrush.
The cost of that rigor is price and access. Evertune's published pricing starts around $800/month, and entry engagements for category-level enterprise research run closer to $3,000/month, sold through a demo rather than self-serve signup. That puts it out of reach for a small team, and it's the right tradeoff only if you need a score you can defend to a board or an auditor, not just a directional read.
Profound was named a Representative Vendor in Gartner's first Market Guide for Answer Engine Visibility Tools, the same report that named Evertune and Semrush. The company raised a $96 million Series C in February 2026 at a $1 billion valuation, bringing total funding to $155 million and putting it past 700 enterprise customers, per Fortune's reporting.
Methodologically, Profound runs prompts once per day per configured model rather than repeating each one dozens of times, per the Authoritytech.io audit cited above. That's a reasonable design for a tool tracking daily trend lines across a large number of brands and engines, but it means Profound's score is not directly comparable to Evertune's higher-repetition sampling, even when both are scanning the same brand. Profound's tier structure and exact per-plan pricing sit behind a sales process at the level this post covers; our full head-to-head against Otterly and Scrunch AI breaks down the entry-tier numbers.
Peec AI tracks brand performance across ChatGPT, Perplexity, and Gemini, and structures its plans as four tiers, Starter, Pro, Advanced, and Enterprise, billed monthly or annually, with Enterprise sold on request. The company states it's trusted by 3,000+ brands and agencies on its own pricing page, and bundles prompt management, competitor suggestions, and gap analysis alongside the raw tracking numbers.
Peec doesn't publish exact dollar figures for its Starter through Advanced tiers on its public pricing page, so budget by requesting current numbers directly rather than assuming a price here; that opacity is itself worth noting given how much this post's methodology section leans on vendors publishing their numbers.
AthenaHQ's Self-Serve plan runs $295/month, or $245/month billed annually, for 3,600 credits and tracking across up to 8 LLMs, with a discounted $95 introductory first month, per pricing coverage from Get Ryze. That's meaningfully broader engine coverage than the four-engine trackers on this list, at a price a small team can put on a card without a sales call.
Otterly's Lite plan is $29/month ($25/month billed annually) for 15 tracked prompts across four engines, ChatGPT, Google AI Overviews, Perplexity, and Microsoft Copilot, per Otterly's current pricing page. Claude, Gemini, and Google AI Mode are available as paid add-ons on top of any tier rather than included by default. Standard ($189/month) and Premium ($489/month) scale up the prompt count to 100 and 400 respectively and add API and MCP access; Enterprise starts at $1,000/month for custom tracking and SSO.
If your budget for this category is closer to "the price of a coffee subscription" than "a line item," Otterly is the realistic floor for a tool that's actually a subscription, not a one-time check. Our full comparison against Profound and Scrunch AI covers the add-on math in more detail.
Scrunch's Starter plan runs $250/month billed annually (or $300/month month-to-month) for 3 seats and 350 custom prompts, tracking ChatGPT, Claude, Gemini, Perplexity, Google AI Mode and AI Overviews, and Meta, per Scrunch's current pricing page. Growth steps up to $417/month annually ($500 month-to-month) for 5 seats and 700 custom prompts. Scrunch is now positioned as a Sitecore product, confirmed on Scrunch's own site, which is worth knowing if you're evaluating it as a standalone vendor versus part of a larger enterprise content-management stack.
Scrunch tracks Claude by default on every tier, which is a real point in its favor against tools that gate Claude behind an add-on or skip it entirely.
Semrush's AI Visibility Toolkit costs $99/month per domain, billed annually, for 25 tracked prompts across ChatGPT, Google AI, Gemini, and Perplexity, with daily, weekly, or monthly refresh options, per Semrush's own pricing page. Semrush doesn't publish its prompt-generation methodology, so you're trusting the panel it builds for you rather than auditing it yourself.
The pitch here is the same one Ahrefs makes below: if you already run Semrush for keyword research and site audits, adding AI visibility inside the same login is convenient. It's a directional read, not a rigorously sampled one, and Semrush was also named alongside Profound and Evertune in the 2026 Gartner Market Guide, for what that recognition is worth at this price point.
Ahrefs Brand Radar folds AI visibility tracking into the Ahrefs SEO suite you may already pay for. Two things stand out here that buyers should weigh before treating its number as directly comparable to a dedicated tracker's.
First, Brand Radar does not track Claude at all, a gap confirmed on Ahrefs' own product page. Second, and more consequential, Brand Radar's own methodology documentation states it does not filter out hallucinated or malformed links, calling them real model output rather than noise to be cleaned up, per Ahrefs' own blog. That's a defensible design choice if you want to see everything a model actually produced, but it means a chunk of what Brand Radar counts as a "mention" may be a link the model invented, not a real citation. Our full breakdown of Brand Radar against dedicated GEO tools covers the pricing stack and an independent undercounting test in more depth.
Rankscale's Pro plan is $99/month for 1,200 credits, tracking up to 4,800 AI engine answers across 10 brand dashboards, per Rankscale's current pricing page. That same plan tracks ChatGPT, Gemini, Perplexity, Claude, DeepSeek, Mistral, Grok, and Microsoft Copilot, matching AthenaHQ's 8-engine spread at roughly a third of AthenaHQ's entry price, plus 50 included page audits. Growth steps up to $385/month for 5,500 credits and 22,000 tracked answers with white-label and API access; Enterprise runs $780/month for 12,000 credits.
If engine breadth per dollar is your primary filter, and Claude coverage without an add-on fee matters, Rankscale's entry tier is the strongest match on this list.
HubSpot's AI Search Grader is free, requires no account, and has no cap on how often you can run it, scoring a brand on five dimensions across ChatGPT, Gemini, and Perplexity, per HubSpot's own product page. It's a snapshot, not a subscription: rerun it manually whenever you want a fresh read, rather than getting a scheduled, ongoing feed the way a paid tracker provides.
That makes it the right first move for a team that hasn't established whether AI visibility is even a real gap yet. Mod Op's free GEO 50 audit tool plays a similar role, and running both before paying for anything is a reasonable way to decide whether this category is worth a line item at all. A Goodfirms survey of 100 marketers from April 2026 found 89% of brands already appear in AI search results while only 14% actually track their AI citations, which is exactly the gap a free grader closes for zero budget.
Ask these before a demo turns into a contract. A vendor that answers all four plainly is rare enough, per the CitedIndex census, that a straight answer is itself a signal.
Ask directly: does the reported score divide by every response in the prompt panel, or only by the responses that mentioned a brand at all? As the denominator problem above shows, the same underlying mentions can produce a 50% or an 83.3% score depending on the answer, with zero change in actual performance.
A single pass per prompt carries a wide margin of error, roughly plus or minus 9 points on 100 distinct single-shot prompts by Evertune's own disclosed testing, while 100 repetitions per prompt narrows that to about plus or minus 1 point. Ask whether the vendor runs each prompt once per day (Profound's approach) or repeats it many times per cycle (Evertune's), and treat the two numbers as measuring different things rather than comparable scores.
A vendor willing to show you the exact prompts behind your score, and let you edit or add your own, is giving you an auditable input. A vendor that shows only the output number is asking you to trust a black box, and remember that naming your own brand inside a prompt can inflate the apparent result by 20-plus percentage points, so a panel you can inspect matters more than one you're told is proprietary.
A retrieval is a model fetching a page during generation. A mention is the brand's name appearing anywhere in the output text. A citation is a mention tied to an explicit source attribution the user can see. Ahrefs Brand Radar's decision to count hallucinated and malformed links as real output, disclosed on its own methodology page, is one concrete example of how much these definitions can vary vendor to vendor, and why a "visibility score" from one tool and another can't be stacked in the same spreadsheet without knowing which of the three each one is actually counting.
None of the 10 tools above rewrite a page. That's not a criticism, it's outside their job description: they measure whether and how you're cited, and every one of them stops at the report. If a vendor's dashboard shows a real gap, brand-mentioning responses trending down, a competitor's page getting cited instead of yours, someone still has to write and ship the page that closes it.
That's the job Lyra does, and it's a genuinely different job from anything in this list: she researches topics worth ranking for, writes in your blog's existing voice, fact-checks every claim the way this post tries to, and opens a pull request for a human to review and merge, rather than auto-publishing anything. She runs on your own Anthropic API key at cost, the same "what does this actually cost" framing this post applies to every visibility vendor above, and her live pricing starts free for 3 posts a month with no card required.
Be equally honest about the limits. Lyra does not track, score, or monitor AI visibility in any form, so if what you actually need is "which of these 10 tools cites us, and how often," Lyra gives you nothing directly there; you still need one of the vendors ranked above. And if your pages are already strong and your only real gap is watching a score over time, there's no reason to add a content pipeline to your stack at all. The two categories, measurement and content, solve different halves of the same problem, and this post's premise, the split between "where do I stand" and "how do I move," is the same one our best AEO tools roundup covers from the content-tools side; AI citation tracking covers the free, DIY version of the measurement half if a paid vendor is more than you need yet.
Before you sign anything, run the vendor's next sales deck or demo through this checklist. It takes ten minutes and it's the same set of questions this post has been building toward.
| Question | What a good answer looks like | What a bad answer looks like |
|---|---|---|
| Denominator | Names it explicitly: "all responses" or "brand-mentioning responses only" | Shows a percentage with no stated base |
| Sample size per prompt | States a repetition count and a margin of error | "We check regularly" with no number |
| Refresh cadence | Names a schedule: daily, weekly, per-cycle | Vague "continuous monitoring" |
| Prompt panel visibility | Lets you view and edit the actual prompts | Score only, prompts described as proprietary |
| Citation definition | Distinguishes retrieval, mention, and citation | Uses all three words interchangeably |
| Hallucinated link handling | States whether invented links are filtered or counted | Doesn't address it at all |
| Engine coverage | Lists exact engines, notes any gated behind add-ons | "All major AI platforms" with no list |
| Published accuracy evidence | Points you to a methodology page or whitepaper | Cites a customer testimonial as proof |
A vendor that clears most of this list has earned the benefit of the doubt on its score. One that clears none of it isn't necessarily lying, per the CitedIndex census, 43% of the category makes no accuracy claim at all rather than a false one, but you should treat its number as a rough directional read, not a figure to report upward as fact.
Whichever vendor's score you end up trusting, closing the gap it finds still takes a page written, fact-checked, and shipped, which is the half of this problem the tools above don't touch.
FAQ
It's a category of tools that run a set of prompts against ChatGPT, Perplexity, Gemini, Google AI Overviews, and similar engines on a schedule, then report whether and how often a brand gets mentioned or cited in the answers. Some crawl AI-generated search results, some sample prompts directly against model APIs, and a few blend both. The output is usually a visibility score or share-of-voice number, but as this post covers, that number means different things depending on how the vendor built it.
There isn't one best pick, because the vendors split by budget and by how rigorously they show their sampling math. Evertune is the strongest choice for teams that want to see the actual methodology behind a score. Profound covers the most engines and carries Gartner recognition, at an enterprise price. Otterly and Rankscale are the cheapest way to get a real subscription with daily-ish tracking. HubSpot's AI Search Grader is free and answers the first question, am I visible at all, before anyone pays for anything. If you've already narrowed the field to Profound, Otterly, and Scrunch AI specifically, our [dedicated comparison of those three by team size](/blog/profound-vs-otterly-vs-scrunch-ai/) goes deeper than this full-field ranking does.
From free to several thousand dollars a month. HubSpot's AI Search Grader costs nothing and requires no account. Entry subscriptions from Otterly and Rankscale start under $100/month. Mid-market tools like AthenaHQ and Semrush's AI Visibility Toolkit run $99-$295/month. Ahrefs Brand Radar's realistic floor is $828/month once its required base Ahrefs plan is included. Evertune's published pricing starts around $800/month and enterprise engagements run closer to $3,000/month. All figures are current as of this post's publish date and vendors change pricing without much notice, so check the vendor's live pricing page before you budget against any number here.
It's the fact that a visibility score changes depending on what a vendor divides by, even when the underlying brand performance hasn't moved. The same 50 brand mentions out of 100 total AI responses can be reported as a 50% score if the denominator is every response, or an 83.3% score if the denominator is only the responses that mentioned any brand at all. Two vendors can scan the same brand on the same day and report two different numbers, both technically true, because they picked a different denominator. Ask any vendor which one they use before you compare their number to anyone else's.
Yes. HubSpot's AI Search Grader is free, requires no account, and can be run as often as you want, scoring a brand across ChatGPT, Gemini, and Perplexity on five dimensions. Mod Op's GEO 50 audit tool is a similar free, one-time entry point. Neither replaces continuous tracking, but both are a legitimate way to find out where you stand before you commit budget to a subscription.
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