Key takeaways
First, a disambiguation that wastes a lot of people's time. LLM observability is telemetry for models your application calls: tokens, latency, traces, cost. Datadog, LangSmith, Helicone. LLM visibility is what models say about your brand: whether ChatGPT names you, which sources it cites, how you move against competitors. Same search terms, unrelated products. This is about the second one.
Search Console will not show you any of it, because the surface that decides whether ChatGPT names your brand is the answer generated before anyone clicks.
Mention rate is the share of sampled answers naming your brand at all. Everything else is a cut of this number. A platform that shows a composite "visibility score" without the mention count underneath is asking you to trust a black box, and that score is not comparable to anyone else's.
Share of voice is your mention rate against the competitors named on the same prompts. It only means something if you control the competitor list, so check whether you define it or the platform infers it from your category.
Citation rate and position is whether the answer links your domain and where you sit in the source list. Mention and citation move independently. Your brand can be named in an answer that cites someone else entirely, and a tool that collapses the two hides the more actionable half.
Sentiment and accuracy is whether the answer describes your product correctly. Newest of the four, least standardised across vendors, so it deserves the most scrutiny in a demo.
Per-engine, not pooled. In our own cross-engine monitoring, citation overlap between engines on the same prompt runs under 30%. A single average across six engines can hide being invisible on the one that matters to your category.
Two numbers for scale before the tools. Ahrefs measured position-1 organic CTR falling roughly 58% on queries that trigger an AI Overview, and per a Globe Newswire industry report only 14% of marketers track AI citations while 89% of brands already appear in AI answers.
| Tool | From | Best for | Notable |
|---|---|---|---|
| Gauge | $599/mo | B2B SaaS doing GEO | Prompt intelligence, Action Center recommendations |
| Profound | $5,000/mo | Enterprise compliance | 10+ engines, sentiment, ISO-certified |
| Peec AI | $2,000/mo | Competitor depth | Real-time citations, clickstream correlation |
| Brandlight | $2,000/mo | Accuracy alerting | Brand accuracy monitoring, health scoring |
| DemandSphere | Custom | Layering AI onto an existing search programme | AI citations on the same footing as SERP share of voice |
| Nightwatch | Contact | Agencies already rank tracking | Citation Intelligence, unlimited seats, white-label on all plans |
| SE Ranking | $119/mo | SMBs on a budget | AIO and AI Mode trackers, competitor citation gap |
| Semrush AI Toolkit | $99/mo add-on | Existing Semrush users | 25 prompts, up to 9 competitors |
| AthenaHQ | $199/mo | Growing SaaS and agencies | Clean dashboard, share of voice, weekly trends |
| OtterlyAI | $29/mo | Monitoring plus optimisation | ChatGPT Ads and Shopping tracking, GEO audit |
| Scrunch | Custom | AI-native reporting | Answer-first reporting, competitor benchmarking |
| Evertune | Custom | Prompt-level analytics | Share of voice broken down per prompt |
| aiclicks.io | $39/mo promo | Tracking plus content in one place | Built-in writer, GSC integration, prompt database |
| Authoritas Visibility Explorer | $99/mo | Multi-client agencies | Daily difference reports, 30+ markets |
| Ahrefs Brand Radar | $129/mo | Existing Ahrefs users | 243M+ monthly prompts, joined to Ahrefs SEO data |
Metric sets cluster by product focus rather than price. Evertune and AthenaHQ are built around share of voice. Peec AI and Nightwatch lead on citation-level detail. Profound and Brandlight are the two treating sentiment and brand accuracy as first-class rather than as add-ons. DemandSphere is the one that puts AI citations on the same foundation as the search-visibility number you already report to the business.
The gap that shows up in testing is engine coverage versus claimed engine coverage. Most cover ChatGPT, Perplexity, Gemini and AI Overview. Copilot and AI Mode are spottier, and depth varies even where the logo appears on the pricing page. Ask whether each engine is queried through an official API or browser automation, because API-only coverage structurally misses AI Overview and Perplexity's web-search surface, which are the citation-heavy ones.
Worth knowing before you read anyone's citation-gap report: per Otterly's analysis of over a million citations, 73% of sites carry technical barriers (robots.txt blocks, CDN rules, JS-only content) that stop AI crawlers reaching the page at all. Some "citation gap" findings are crawler-access findings wearing a different hat.
One B2B SaaS brand and one consumer-product brand, 25 commercial-investigation queries each, four weeks, spanning comparison, definitional, recommendation and how-to intents. We captured ground truth by hand across ChatGPT with web search, Perplexity, the Gemini app, Copilot, AI Overviews and AI Mode, then compared what each tool reported for the same queries on the same days. Scored on engine coverage, citation fidelity, update frequency, reporting depth, pricing fairness, and methodology transparency.
This roundup is published by cloro, a data provider rather than a dashboard, so none of the 15 tools above is a competitor. cloro appears once below, in the infrastructure section, and is not scored against them.
A roundup is editorial judgement on one date. The AI visibility platforms leaderboard is the standing measurement beside it, re-scored weekly and free to cite.
Vendors use "API" for three different things:
Two things a comparison table cannot hold. API availability moves between pricing tiers often enough that any published list is stale within a quarter. And an API on a dashboard product is normally rate-limited against the prompt allowance you already bought, so "has an API" does not mean "can back a product feature."
Three bands, from watching teams do this:
The builds themselves are small. Three patterns cover most of it: nightly scheduler into Postgres into Metabase with Slack alerts; per-client query lists with per-tenant keys and white-label reports; or a daily batch into BigQuery joined against GA4, Search Console and CRM data. Each is one to two weeks of engineering on top of a managed data layer.
What is not small is the parsing layer. Scraping it yourself runs $500-2,000 a month in proxies and browser infrastructure plus two to three months of build, and the hard part is not the scraping. It is that a citation parser working cleanly in January can miss 30% of them by April after a UI change, silently. Official model APIs avoid that and cannot help either, because they do not expose citation data the way a live search surface does. cloro is one such layer: a single endpoint across ChatGPT, Perplexity, Gemini, Copilot, AI Overview, AI Mode, Google Search and Google News, returning parsed source URLs with position and label, plus query fan-out terms, as JSON rather than screenshots. Credits are deducted only on successful extraction. Hobby is $100/month for 250,000 credits ($0.40 per 1,000), scaling to $0.34 at the top tier, with 500 free credits to test and docs rather than an onboarding call. Async is the right call for nightly batches; sync costs two extra credits and returns inline.
It is not a dashboard, and if what you want is a dashboard then one of the 15 above is the better purchase.
If you want the ChatGPT surface specifically rather than all six, monitoring ChatGPT mentions is covered end to end separately.