LLM visibility tracking tools: 15 compared, and when to build instead

Key takeaways

• LLM observability and LLM visibility are unrelated products sold under overlapping search terms. This is about the second one.
• Citation overlap between engines on the same prompt runs under 30%, so a pooled six-engine average can hide being invisible on the engine your category actually uses.
• Build-versus-buy flips around 50,000 queries a month or 10 tracked clients. Below 5,000 a dashboard wins on time-to-value alone.

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.

What do LLM visibility tracking tools actually measure?

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.

The 15

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.

How we tested

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.

The API question, which most comparisons get wrong

Vendors use "API" for three different things:

• Scheduled export. CSV drops, Sheets, sometimes a Looker Studio connector. Fine for reporting, useless for a product feature. This is the most common thing sold as integration.
• Read API over their stored data. Query the vendor's results for your tracked prompts. Good for pulling their numbers into your BI stack, still bounded by the prompts, engines and cadence your plan allows.
• Raw data API. Submit any prompt to any surface, get the parsed response. Different product category, not a dashboard feature.

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."

When building your own starts to make sense

Three bands, from watching teams do this:

• Under ~5,000 queries a month. Buy a dashboard. Anything you build will take longer to stand up than the value it returns.
• Over ~50,000 queries a month, or 10+ tracked clients. Per-seat dashboard pricing crosses per-call API pricing, and an in-house build pays back in two to four months.
• In between. It depends entirely on whether you already have BI and ops infrastructure to host the output. If the answer is a CSV landing in a warehouse, you are paying for a UI layer and throwing it away.

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.

What to pressure-test in a demo

• Engine coverage method. Official API or browser automation, per engine. Vagueness here is the answer.
• Citation format. Structured queryable URLs, or screenshots. This decides whether you can compute anything yourself.
• Query control. Can you import prompts from Search Console and define your own, or does it generate them from your domain?
• Cost at your real cadence. Affordable at 50 queries a week is a different product at 500. Most teams find they need weekly rather than daily, which changes the maths a lot.

If you want the ChatGPT surface specifically rather than all six, monitoring ChatGPT mentions is covered end to end separately.