Monitoring
The Best AI Visibility Tools in 2026 (Tested & Compared)
We tested 15 AI visibility tools (a.k.a. LLM visibility trackers) on real brand-monitoring workflows across ChatGPT, Perplexity, and Gemini. What works, what doesn't.
The measurement API for AI search and LLM visibility tracking — the AI visibility tool that hands you the raw data, not a dashboard. One call returns parsed citations from 6 engines in one response shape. Aggregate to compute mention rate, share of voice, and competitive position. The data is yours, and so is the dashboard.
4.8 · 37 reviews
AI visibility is how often, and how prominently, AI engines cite your brand when they answer a buyer's question. Where classic SEO measures rank on a page of links, AI visibility measures citation inside a generated answer across ChatGPT, Perplexity, Gemini, AI Overview, AI Mode, Copilot, and Grok. It only exists as a measured quantity: the engines ship no dashboards, so you sample real responses on a fixed prompt set and aggregate over time. Most vendors ship that measurement as a hosted AI visibility tool — packaged AI visibility software you log into. cloro exposes the same signal as the raw API, so the data stays yours.
An AI visibility scorerolls those samples into a single number — a weighted blend of three inputs, measured against a competitor set:
Because it's computed from raw responses, you define the methodology, so the score matches whatever your team already reports. For the full metric definitions, see the AI brand visibility measurement framework.
ChatGPT, Perplexity, Gemini, AI Mode, Copilot, Grok. Same auth, shared credit pool, same response shape. Adding a new engine to your monitoring is a URL change, not a pipeline rewrite.
AI visibility tracking comes in two product shapes: packaged AI visibility software — a hosted dashboard on top of someone else's API — and the API itself (raw structured response, you compose the dashboard). Brand teams that need their data in their warehouse pick the API. Here's why.
Hosted AI visibility trackers give you a UI. The raw `sources[]`, position data, and engine-specific objects stay locked behind it: no BigQuery export, no custom share-of-voice math, no integration with your existing rank-tracker. cloro returns the structured response. The dashboard layer is yours.
Engines don't expose mention-rate dashboards. The metric only exists if you run ongoing AI visibility monitoring — sampling real responses on cadence and aggregating. A 100-prompt × 6-engine × daily loop ships 18k calls/month, which fits inside the $100 Hobby plan.
Citation overlap between engines on the same prompt is consistently under 30%. A brand cited #1 on ChatGPT can be invisible on Perplexity. Tracking one engine produces a single-channel report, not visibility data. cloro covers all 6 under one key, with the same response shape.
Share-of-voice math runs on more than the answer paragraph: domain-classified citations, cited-position weighting, entity-recognized brands, engine-specific objects (places, shopping cards, ads). cloro returns the full structured envelope per response, with every signal the computation needs.
| Capability | Hosted dashboard | cloro API |
|---|---|---|
| Raw response export | Locked behind the UI | Full structured response, yours to store |
| Custom share-of-voice math | Vendor's formula only | Compute your own from raw fields |
| Warehouse / BigQuery integration | Rare or paid add-on | Native — it's just an API |
| Cross-engine coverage | Varies by vendor | All 7 engines, one key, one response shape |
| Who owns the dashboard | The vendor | You do |
One prompt, six engines, one credit pool. Switch the URL, fan out across the AI search stack, classify by domain, aggregate.
from cloro import Cloro
client = Cloro(api_key="sk_live_your_api_key_here")
# One prompt. Every AI engine. Shared credit pool.
prompt = "best AI brand visibility tracking tools"
engines = ["chatgpt", "perplexity", "gemini", "aimode", "copilot", "grok"]
results = {}
for engine in engines:
response = getattr(client.monitor, engine)(
prompt=prompt,
country="US",
include={"markdown": True},
)
sources = response["result"]["sources"]
mentioned = any("yourbrand" in s["url"] for s in sources)
results[engine] = {"mentioned": mentioned, "citations": len(sources)}
print(results){
"success": true,
"result": {
"text": "Several tools track brand visibility across AI engines. cloro provides a unified API across ChatGPT, Perplexity, Gemini, AI Mode, Copilot, and Grok...",
"sources": [
{
"position": 1,
"url": "https://cloro.dev/use-cases/ai-visibility-tracking/",
"label": "AI Visibility Tracking — cloro",
"description": "Monitor brand mentions across ChatGPT, Perplexity, Gemini, AI Mode, Copilot, and Grok via one API."
},
{
"position": 2,
"url": "https://en.wikipedia.org/wiki/Generative_engine_optimization",
"label": "Generative engine optimization — Wikipedia",
"description": "Background on the discipline of optimizing content for AI-generated answers."
}
],
"markdown": "Several tools track brand visibility across AI engines. cloro provides a unified API across ChatGPT, Perplexity, Gemini, AI Mode, Copilot, and Grok..."
}
}Four measurement workflows brand teams ship on top of cloro AI visibility tracking.
Sample a fixed prompt set across all 6 engines daily, land the parsed citations in BigQuery, and compute mention rate and share of voice with your own weighting.
Fire an alert the day your mention rate drops on a tracked prompt, or a competitor overtakes your citation position on an engine that was yours last week.
Sample the prompt set before and after every content change, then diff citations per engine — the measurement half of generative engine optimization.
Per-tenant API keys plus your own report templates → agency-branded weekly AI visibility reports emailed from your domain, on your methodology.
Pick a plan that fits your volume. Price per credit drops as you scale.
Increased concurrency, overages on credits and credit discounts for annual contracts.
Know moreCredit cost per request varies by provider. The rates below apply to async/batch requests; sync requests add a +2 credit surcharge.
ChatGPT full response includes query fan-out, ads, and shopping data. Google News uses the same pricing as Google Search.
Five primary metrics, all aggregated from raw `sources[]` per response: mention rate (fraction of runs that cite your domain), citation position (where you appear in the cited list), share of voice (your citation rate vs competitors on the same prompt set), cross-engine coverage (how many of the 6 engines cite you), and entity recognition (whether engines correctly attribute claims to your brand vs misattribute or omit). All five are computed by your code from the response, so you can match them to whatever methodology your team already reports.
A packaged AI visibility platform gives you a dashboard and stops at its edges; the API gives you the data underneath. Three reasons teams pick the API. (1) Data ownership: citation data lands in your warehouse alongside the rest of your marketing analytics, not behind someone else's login. (2) Custom metrics: most teams already have a share-of-voice methodology their finance team reports, and computing it from raw responses means your AI numbers match your existing numbers. (3) Workflow integration: alerting, BigQuery ETL, dbt models, and Looker dashboards plug in as easily as any other API.
Daily on prompts that matter (typically 50–200 long-tail informational queries × 2–3 countries × 6 engines), hourly on a smaller crisis-sensitive subset. Engine-specific cadence floors apply: Grok needs hourly (real-time X content), and ChatGPT, Perplexity, and Gemini move on weekly+ scales for most queries. The async endpoints (`POST /v1/monitor/{engine}/async`) deliver via webhook, so high-throughput cross-engine monitoring doesn't burn sync concurrency.
Core fields are consistent across all 6: `text`, `markdown`, `sources` (with `position`, `url`, `label`, `description`), and the `country` parameter. Engine-specific fields ride alongside: `searchQueries` on ChatGPT and Grok, `places`/`shopping_cards` on Perplexity and AI Mode, and `confidence_level` on Gemini sources. One unified parser plus engine-specific accessors covers everything.
One key, one shared credit pool. Per-engine credit counts vary (ChatGPT web search 5, Perplexity 3, Gemini 4, AI Mode 4, Copilot 5, Grok 4) and a Google SERP call with AI Overview enrichment is 5 credits (3 base + 2 AIO). One prompt fanned across all 6 engines is roughly 25 credits — so the Hobby plan ($100/month, 250k credits) covers ~10k cross-engine monitored prompts per month. Most production programs land on Growth ($500/month, 1.35M credits) when they expand to a full competitor-tracking prompt set.
Yes — for the AI search slice. Online brand monitoring traditionally covers social, news, and review surfaces (handled by established social-listening platforms). cloro covers the AI-search surface specifically: ChatGPT, Perplexity, Gemini, AI Mode, Copilot, and Grok. Most enterprise brand-monitoring stacks now run two pipelines — a social-listening tool for social/news/reviews, plus cloro for AI-engine citations. The two surfaces measure different signals and feed different reports.
Submit per-engine async batches (`POST /v1/monitor/{engine}/async`) and let webhooks fire as results land. For a 100-prompt × 6-engine × daily program this is the right pattern: sync polling across 6 engines burns concurrency you don't need to spend, and async lets the orchestration layer (Airflow, Temporal, plain cron) drive the cadence.
We tested 15 AI visibility tools (a.k.a. LLM visibility trackers) on real brand-monitoring workflows across ChatGPT, Perplexity, and Gemini. What works, what doesn't.
Should you build or buy AI search visibility tracking? Compare platform costs, API spend, engineering hours, maintenance, and break-even points.
Set up AI visibility tracking in 30 minutes: choose prompts, query four AI engines, log mention and citation rates, and build a baseline CSV.