What an LLM visibility tracker actually does
An LLM visibility tracker does three things, repeatedly, on schedule. It runs a fixed set of prompts against AI assistants (ChatGPT, Gemini, Claude, Grok, Perplexity, and however many others the platform supports). It parses the responses to identify which brands are mentioned, in what order, with what sentiment, and which URLs the model cited alongside the mention. It rolls those measurements up into a dashboard that lets you watch your visibility move over time and compare yourself to competitors.
The category goes by several names. AI visibility tool, AI search tracker, generative engine optimization (GEO) platform, AI brand monitoring, answer engine optimization (AEO) tracker. They mostly describe the same thing. The cleanest umbrella term in 2026 is "LLM visibility tracker," and the cleanest framing is "this is what rank tracking became."
Features that matter (and the ones that don't)
When you compare LLM visibility trackers, the features that look impressive in screenshots aren't always the ones that determine whether the data is useful. Here's the actual scorecard, with notes on what each item really means.
| Feature | Why it matters |
|---|---|
| Engine coverage | Different AI assistants cite different sources. Cross engine overlap is only ~11%. |
| Prompt volume per plan | 25 to 50 prompts per category is the sweet spot for stable variance. Plans that cap at 15 force prioritization. |
| Data refresh frequency | Daily versus weekly. Weekly refresh quietly serves stale answers; in a fast moving category that's misleading. |
| Citation and source tracking | You can't improve what you can't see. The URLs the model cites are your roadmap for outreach and content. |
| Sentiment analysis | Being mentioned isn't enough. Being mentioned favorably is what moves revenue. |
| Position weighting | First mention is worth more than fifth. Trackers that don't weight position misreport competitive standing. |
| Competitor benchmarking | Share of voice across the same prompt set is the only honest read of where you sit in the category. |
| Historical trend data | Whether the platform stores enough history to spot a sentiment swing or a competitor pulling ahead. |
| GA4 / revenue attribution | Ties AI mentions to actual traffic and revenue. The hardest integration to fake, the easiest to need. |
| Alerts (Slack / email) | Universal at this point. The difference is real time versus batched, and how granular the trigger rules get. |
| API and webhooks | If you want to push AI visibility data into your warehouse or trigger workflows, this is non negotiable. |
| Recommendations engine | Dashboards show you the gap. Recommendations close it. The strongest trackers do both. |
- Engine coverage
Different AI assistants cite different sources. Cross engine overlap is only ~11%.
- Prompt volume per plan
25 to 50 prompts per category is the sweet spot for stable variance. Plans that cap at 15 force prioritization.
- Data refresh frequency
Daily versus weekly. Weekly refresh quietly serves stale answers; in a fast moving category that's misleading.
- Citation and source tracking
You can't improve what you can't see. The URLs the model cites are your roadmap for outreach and content.
- Sentiment analysis
Being mentioned isn't enough. Being mentioned favorably is what moves revenue.
- Position weighting
First mention is worth more than fifth. Trackers that don't weight position misreport competitive standing.
- Competitor benchmarking
Share of voice across the same prompt set is the only honest read of where you sit in the category.
- Historical trend data
Whether the platform stores enough history to spot a sentiment swing or a competitor pulling ahead.
- GA4 / revenue attribution
Ties AI mentions to actual traffic and revenue. The hardest integration to fake, the easiest to need.
- Alerts (Slack / email)
Universal at this point. The difference is real time versus batched, and how granular the trigger rules get.
- API and webhooks
If you want to push AI visibility data into your warehouse or trigger workflows, this is non negotiable.
- Recommendations engine
Dashboards show you the gap. Recommendations close it. The strongest trackers do both.
Engine coverage: count the surfaces, not the brand names
The honest count of "AI engines worth tracking" in 2026 is closer to 10 surfaces than to 5 engines. The five everyone knows are ChatGPT, Gemini, Claude, Grok, and Perplexity. After those, Microsoft Copilot, Meta AI, and DeepSeek are large enough to matter in a global brand audit.
The wrinkle is that Google ships at least two AI answer surfaces (AI Overviews in classic search, AI Mode as a standalone surface) and they cite differently. Tools that fold both into "Gemini" underreport your Google AI visibility because the answers and citation graphs are different. The same applies to ChatGPT free versus ChatGPT Plus: browsing behavior is gated by tier and a tool that doesn't test both can mislead.
Cross engine overlap of cited domains. The brand that dominates ChatGPT often has no presence in Perplexity, and the reverse. A tracker that covers one engine well is reporting on a slice, not the picture.
Pricing models in this category
Three pricing patterns are common, and each pushes you toward different usage. Worth understanding which one a vendor uses before signing up.
- Per prompt. Profound, Peec, Otterly. Each tracked prompt counts against your plan. Adding engines sometimes multiplies the count.
- Per credit. AthenaHQ. One AI response is one credit. Multi engine tracking burns credits fast and the bill is harder to predict.
- Per brand or per domain. Smaller tools and some agency focused products charge by brand tracked.
The useful normalizer is per prompt cost. By that measure: Profound Lite is about $10 per tracked prompt per month (50 prompts at $499). Peec Pro is closer to $2.40. Otterly Lite is about $1.93. Kodo is free during private beta. Engine coverage matters here, because $10 a prompt across 8 engines is different value than $10 across 3 engines.
How the leaders compare
Six LLM visibility trackers cover most of what serious 2026 buyers consider. Here's the side by side.
| Tool | Positioning | Entry | Engines | Refresh | Notable |
|---|---|---|---|---|---|
| KodoUs | Developer first, agent era platform | Free during beta | 8 | Real time | Transparent score, multi agent system, REST + webhooks day one |
| Profound | Enterprise leader | $499/mo Lite | 5 | Daily | Conversation Explorer, prompt volume data, Fortune 500 logos |
| AthenaHQ | Action Center, not just dashboards | $270/mo Lite | 8 | Daily | Generates tasks from gaps. Google/DeepMind founder pedigree |
| Otterly.AI | Cheapest entry | $29/mo Lite | 4 | Weekly | Gartner Cool Vendor 2025. Semrush partnership |
| Peec AI | Agencies and multi language | €85/mo | 5 | Daily | 115+ languages, Looker Studio Connector on every plan |
| Ahrefs Brand Radar | Bundle into existing Ahrefs SEO suite | +$50/mo on Ahrefs | 3 | Weekly | 199M-prompt dataset. Deep SEO crossover |
- KodoUs
Developer first, agent era platform
- Entry
- Free during beta
- Engines / Refresh
- 8 / Real time
Transparent score, multi agent system, REST + webhooks day one
- Profound
Enterprise leader
- Entry
- $499/mo Lite
- Engines / Refresh
- 5 / Daily
Conversation Explorer, prompt volume data, Fortune 500 logos
- AthenaHQ
Action Center, not just dashboards
- Entry
- $270/mo Lite
- Engines / Refresh
- 8 / Daily
Generates tasks from gaps. Google/DeepMind founder pedigree
- Otterly.AI
Cheapest entry
- Entry
- $29/mo Lite
- Engines / Refresh
- 4 / Weekly
Gartner Cool Vendor 2025. Semrush partnership
- Peec AI
Agencies and multi language
- Entry
- €85/mo
- Engines / Refresh
- 5 / Daily
115+ languages, Looker Studio Connector on every plan
- Ahrefs Brand Radar
Bundle into existing Ahrefs SEO suite
- Entry
- +$50/mo on Ahrefs
- Engines / Refresh
- 3 / Weekly
199M-prompt dataset. Deep SEO crossover
Public positioning and pricing as of May 2026. See Kodo vs Profound for a deeper head to head.
What goes wrong with LLM visibility trackers
The complaints that show up consistently in G2 reviews, forum threads, and customer interviews don't usually concern the features each tool ships. They concern the gap between what the tool reports and what the buyer needed.
The five recurring patterns:
- Weekly refresh, stale data. Most entry plans refresh weekly. Customers often don't realize the dashboard is 5 to 7 days behind real ChatGPT and Gemini answers until they cross check manually and find the dashboard wrong. In a fast moving category, weekly is too slow.
- Credit pricing produces unpredictable bills. Credit based plans (AthenaHQ being the prominent example) consume credits faster than people expect once multi engine tracking is on. Buyers report the bill landing 2 to 3 times the estimate in month one.
- Opaque, sales led pricing slows you down. Several enterprise vendors won't quote a price without 2 to 3 calls. By the time the contract is signed, 1 to 3 weeks have passed before usable data appears.
- Prompt caps force prioritization. Lite plans with 15 to 50 prompts don't let you track a real category prompt library (which wants to be 25 to 50 per topic, and most brands have 3 to 6 topics).
- Dashboards that don't tell you what to do. Pretty charts are easy. A list of "fix these five things to improve your score by 8 points" is hard. Several tools stop at the dashboard. Athena's Action Center and Kodo's Advisor agent are two attempts to close that gap.
The share of practitioners who said in a 2026 survey that their current LLM visibility measurement is inaccurate. The category is young; data quality varies more than dashboards do.
How to pick a tracker for your situation
The right tracker depends less on the feature list and more on what you're optimizing for. A short decision tree:
- Large enterprise, procurement process, deepest dataset and Fortune 500 case studies matter: Profound is the safe pick. See our Kodo vs Profound comparison for tradeoffs.
- You want the cheapest functional starting point: Otterly Lite at $29/month. Don't expect Gemini or Google AI Mode in the base.
- You're an agency or you operate across many languages: Peec AI. Looker Studio connector ships on every plan, 115+ languages.
- You want the platform to generate tasks, not just show charts: AthenaHQ's Action Center is the strongest in this lane.
- You're already on Ahrefs and want one bill: Ahrefs Brand Radar as an add on. Engine coverage is narrower than a dedicated tracker.
- You want real time alerts, 8 engine coverage, a transparent score formula, and pricing that starts at free: Kodo, during private beta.
Frequently asked questions
The questions we hear from teams evaluating an LLM visibility tracker for the first time.
What is an LLM visibility tracker?
An LLM visibility tracker is a tool that runs a fixed set of prompts against AI assistants like ChatGPT, Gemini, Claude, Grok, and Perplexity on a schedule, then measures whether your brand is mentioned, where in the answer, with what sentiment, and which sources the model cited alongside you. It's the equivalent of a rank tracker for the AI search era.
How many AI assistants should a tracker cover?
The honest count for 2026 is closer to 10 surfaces than 5 engines. The base five are ChatGPT, Gemini, Claude, Grok, and Perplexity. Then add Microsoft Copilot, Meta AI, and DeepSeek for global coverage. Google AI Overviews and Google AI Mode are best treated as separate surfaces, not as part of Gemini. A tracker that covers fewer than 5 base engines is going to miss large slices of your real visibility.
What features should I evaluate when picking a tracker?
Engine coverage, prompt volume per plan, data refresh cadence (daily versus weekly), citation and source link tracking, sentiment analysis, position weighting (was your brand listed first or sixth), competitor benchmarking, historical trend data, GA4 or revenue attribution, Slack and email alerts, data warehouse export, API access, and whether the tool tells you what to do next or just shows you a dashboard.
How much does an LLM visibility tracker cost?
The market entry in 2026 is $29/month (Otterly Lite). Mid market lands at roughly $100 to $500/month (Peec, AthenaHQ Lite, Profound Lite). Enterprise is $2,000 to $5,000-plus/month and is almost always sales led. Per prompt math is the useful normalizer: Profound Lite works out to about $10 per tracked prompt, Peec Pro to about $2.40, Otterly Lite to about $1.93. Kodo is free during private beta.
Which LLM visibility tracker is best?
Best depends on your situation. Profound for large enterprise with the deepest dataset and a procurement process that values funding history. Otterly for the cheapest functional starting point. Peec for agencies and multi language coverage. AthenaHQ if you want the platform to generate optimization tasks, not just dashboards. Ahrefs Brand Radar if you're already on Ahrefs and want one consolidated bill. Kodo if you want real time alerts, 8 engine coverage, a transparent score formula, and pricing that starts at free.
What's the biggest mistake people make picking a tracker?
Picking on dashboard polish instead of data quality. G2 reviews skew toward UI praise because that's what reviewers notice first, but the thing that breaks LLM tracker programs is stale data (weekly refresh quietly serving 7 day old answers), missing engines (Gemini and Google AI Mode often add ons), and prompt limits that force prioritization rather than tracking your real prompt library. Ask any vendor about refresh frequency, engine coverage, and prompt cost per engine before you compare dashboards.
Does the data refresh daily or weekly?
It varies by vendor and tier. Daily refresh is the standard at the higher tiers and at Kodo across the board. Many tools run weekly refresh on the entry plans, which means your dashboard can be quietly 5 to 7 days behind the real ChatGPT and Gemini answers. For categories where AI answers change frequently (anything with a competitor recently in the news, anything time sensitive), weekly refresh is a real liability.
Do LLM visibility trackers integrate with GA4 or Slack?
Slack alerts and email digests are universal across the major trackers. GA4 integration (so you can connect AI mentions to traffic and revenue) is a Profound and AthenaHQ strength and a gap at the cheap entry plans. Looker Studio connectors are a Peec strength. Data warehouse export (BigQuery, Snowflake) is enterprise tier for most vendors. Kodo ships REST API and webhooks from day one.