I spent a weekend grepping server logs trying to figure out why a chunk of new signups had no referrer, no UTM params, no search query attached at all. They just appeared, direct traffic with zero context. Tracing a few back through support conversations, the pattern was obvious: people had asked ChatGPT or Perplexity for a recommendation, gotten our product name, and typed the URL in by hand.
That's not a traffic source any analytics stack is built to catch, because there's no click to attribute. So I went looking for tools that measure, systematically, whether and how often language models mention a given brand. This category (AEO, GEO, "AI visibility," pick your term) turned out to be small, fast-moving, and priced all over the map.
How this actually works under the hood
Worth understanding the mechanism first, because it explains the pricing spread and why you should distrust any tool promising a precise "rank."
There's no API for "what position am I in ChatGPT's answer to X." Every tool here does some version of the same thing: run a bank of representative prompts against model APIs (or automate the consumer chat UIs, riskier and against most providers' terms) on a schedule, parse the completions for brand names and citations, then aggregate that into a visibility score or share-of-voice metric over time.
Two implications worth knowing before you buy:
- Model coverage drives cost almost linearly. Every extra model (GPT class, Claude, Gemini, Perplexity's retrieval layer, DeepSeek) means a separate API contract, separate rate limits, and separate parsing logic, since citation formats differ per model.
- Sampling is inherently noisy. Same prompt, same model, different day (or the same minute at nonzero temperature) can produce different brand mentions. Good tools sample repeatedly and report trends; weak ones show a single run and call it a score. Treat any number here like a flaky test suite: one red run doesn't mean the build is broken.
With that context, here's what I found after testing or reading through the pricing and docs of about a dozen tools. Full disclosure: I built the first one, AskAiRank. Included on merit, but weigh accordingly.
1. AskAiRank (I built this one)
AskAiRank runs your buyers' actual prompts against ChatGPT, Claude, Perplexity, Gemini, and a handful of other models, then scores how often and how favorably your brand shows up versus competitors.
Free plan covers one brand across 10 prompts on ChatGPT and DeepSeek, no card required, which is enough to sanity-check whether you're worth tracking at all. Paid starts at $19/month; $39/month unlocks all 8 models we support. No enterprise-gated features hiding behind a "contact sales" wall.
2. Otterly.ai
Otterly has been in this space longer than most and keeps its pricing genuinely accessible. The Lite plan is around $29/month for 15 prompts across four engines, with unlimited team seats included, which is unusually generous for the price point.
Good first tool if you've never measured AI visibility before and want a clean dashboard without a lot of configuration overhead.
3. Waikay
Waikay ships a free tier and keeps the UI opinionated: instead of a wall of charts, it surfaces a short list of concrete fixes. Less raw data, more "do this next."
Fits founders who'd rather get a to-do item ("add a comparison page") than interpret a graph themselves.
4. Nightwatch
Nightwatch started as a conventional rank tracker and bolted AI visibility onto its existing local SEO stack as an add-on module. Base plan runs about $32/month; the AI visibility layer pushes the total to roughly $131/month.
Makes sense if you're already inside Nightwatch for regular SEO tracking and want one bill instead of two subscriptions covering adjacent things.
5. Peec AI
Peec is built for SMBs and agencies, with guided onboarding and roughly $95-99/month for 50 prompts and a choice of three out of six supported models, unlimited seats included. Decent middle ground between the cheap tier's narrow coverage and the enterprise tools' everything-included pricing.
6. Profound
Profound is the name that comes up constantly in AEO conversations right now. It closed a large Series C in early 2026 and lists hundreds of enterprise customers. Pricing starts at $99/month (Starter), climbs to $399/month (Growth), and goes custom above that for enterprise contracts.
Overkill for a bootstrapped project, but worth knowing about since it's the tool your investors or advisors are most likely to have heard of.
7. Scrunch
Scrunch targets mid-market teams and explicitly covers every major model, including Claude, which a few competitors either skip or gate behind a higher tier. Starter pricing sits around $250-300/month.
Makes sense once you're past the bootstrap stage and need broad, uniform model coverage without stitching together multiple vendors.
8. Semrush AI Toolkit
If you already have a Semrush subscription, the AI Visibility Toolkit adds brand tracking across ChatGPT, Gemini, Perplexity, and Google AI Overviews for about $99/month per domain on top of your existing plan. It pairs share-of-voice tracking with content recommendations aimed at the kinds of pages that tend to get pulled into AI answers.
A reasonable bolt-on if you're already living in the Semrush ecosystem. A harder sell as a standalone purchase.
9. Ahrefs Brand Radar
Ahrefs added AI visibility as an add-on to its suite, and it's the most expensive option here by a wide margin: $199/month per AI platform index, or $699/month for all six bundled, stacked on top of an active Ahrefs subscription. Realistic full-coverage cost lands north of $800/month.
The tradeoff for that price is breadth: it also tracks Reddit, YouTube, and TikTok mentions alongside the AI engines, something nothing else on this list does.
What I'd actually pick
If you're a solo dev or small team validating whether this matters at all, start with a free tier (AskAiRank or Waikay) or a cheap tool (Otterly) before evaluating anything in the $99-800/month range. You don't need six models sampled daily at launch. You need a repeatable signal: are you showing up when someone asks a model about your category, and is that trend moving up or down over a few weeks.
Once that signal is worth acting on, and once a few hundred dollars a month is a rounding error rather than a real line item, the mid-market and enterprise tools make more sense. Until then, treat this like any other noisy-signal monitoring problem: sample cheaply, sample often, and don't overbuild instrumentation before you know the thing you're instrumenting is worth the attention.
