Best tools for tracking LLM visibility in 2026

What LLM visibility tracking tools do

Every tool in this category runs on the same basic loop: send a defined set of prompts to the major AI models and collect the responses to check whether your brand appears. That's it. There is no secret algorithm, no proprietary signal. ChatGPT, Gemini, Perplexity, Claude, and Grok don't publish mention feeds or brand analytics - so the only way to know if you're showing up is to ask, repeatedly, and record what comes back.

What differs between tools is the number of models covered, how frequently they run the prompts, the quality of the reporting dashboard, and - critically - what they charge you for the privilege. As our full cost breakdown explains, the real cost driver is token volume. One prompt sent to six models returns six lengthy responses, all of which need to be stored and analysed. At 500 prompts, a single tracking run costs $100-$150 in raw API and compute. The SaaS platforms build their margin on top of that.

Understanding the mechanics tells you exactly what you're paying for, so you can decide if it's worth it.

Profound - best for enterprise teams with serious compliance requirements

Profound is the tool most cited at the enterprise end of this market, and for good reason. It covers the widest range of AI engines, tracks hallucination risk alongside standard citation metrics, and is built with regulated industries - finance, healthcare, legal - in mind. If your brand being misrepresented by an LLM carries real legal or reputational risk, Profound's depth justifies the cost.

Profound is priced for enterprise teams with dedicated search or content ops functions, and the feature set reflects that. A solo founder or one-person marketing team will find themselves paying for capabilities they won't use. That said, if you're at the stage where LLM reputation management is genuinely business-critical, Profound is the most thorough option available.

Semrush AI visibility toolkit - best for teams already in the Semrush ecosystem

Semrush extended its core platform into AI search monitoring, which is a sensible move for anyone who already runs their SEO operation inside it. The AI visibility toolkit tracks brand mentions across LLM outputs, connects that data to traditional search performance, and lets you run competitor benchmarking alongside the SEO signals you're already tracking.

The integration angle is genuinely useful - having citation data and organic rank data in the same platform saves context-switching. It's designed as an add-on to the broader Semrush subscription, so the value is clearest for existing users who want to extend what they already have. Semrush subscriptions start at a price point that puts it out of reach for a lot of small teams. For existing users, though, the AI visibility layer is a straightforward addition.

Ahrefs brand radar - best for benchmarking against competitors

Ahrefs built its brand radar feature to answer a specific question: where does your brand stand in AI responses relative to the competition? It tracks mentions across the main LLM platforms and surfaces share-of-voice data that tracks frequency and share-of-voice relative to competitors.

For teams who already use Ahrefs for backlink analysis and content research, Brand Radar slots in without friction. Ahrefs, like Semrush, is a substantial subscription - the brand visibility feature is one part of a much larger product. Worth it if you're an Ahrefs user. A harder sell if you're only here for the LLM tracking piece.

Peec AI - best for citation analysis and agency use cases

Peec AI positions itself as a search console for AI - which is a clean way to describe what it does. It's strong on citation provenance, meaning it shows you which specific content was cited and where in the response it appeared. For agencies managing multiple clients across different categories, Peec's multi-client structure handles that well.

Pricing starts from around €89 per month, which puts it in a more accessible tier than Profound or the major SEO platforms. The depth of citation data is a genuine differentiator if citation-source analysis is central to your strategy - knowing which pages are cited lets you act on the data in ways that mention counts alone don't support.

Otterly.AI - best budget entry point with a subscription model

Otterly is widely referenced as the most accessible entry point in this market, with plans starting around $25-$29 per month. It tracks mentions and sentiment across multiple LLM platforms and is straightforward to set up. For a small business that wants a baseline read on AI visibility without enterprise pricing, Otterly fits the bill.

The trade-off is that it's still a subscription, and the lower-tier plans limit the number of prompts you can track. If your category requires a broader prompt set to get a meaningful picture - which most do - you'll need a higher tier relatively quickly. Otterly is also UI-based rather than API-based, which affects data freshness in ways that shape how you interpret the results. Our piece on how LLM brand tracking works walks through what that distinction means in practice.

Contengi LLM tracker - most cost-effective, pay as you go

Every tool listed above runs on a subscription. You pay monthly whether you run 10 prompts that week or 10,000. Contengi's LLM tracker is the only pay-as-you-go option in this space - you pay for what you actually run, with no subscription sitting idle in the background.

The capability matches the tools it competes with. It covers the same models - ChatGPT, Gemini, Perplexity, Claude, Grok, and Google's AI surfaces - tracks citation rates, brand mentions, share of voice, and sentiment, and surfaces the results in a clean dashboard. The difference is the pricing structure. A solo founder who wants to run a full prompt set on the first and fifteenth of each month pays for two runs. A team that wants daily tracking pays for daily tracking. Nobody pays for the months they're heads-down in product work and not watching the data.

Where Contengi sits relative to the field: it matches the core tracking capability of Peec, Profound, Ahrefs Brand Radar, Semrush, and Otterly, without the subscription overhead. For a small team or solo operator who wants the data without building their own tracker, it's a straightforward option worth considering.

What to look for when choosing an LLM tracking tool

Model coverage is the first thing to check. A tool that only tracks ChatGPT is giving you a partial picture - Perplexity and Gemini have meaningfully different citation behaviours, and covering more models gives you data worth acting on. The tools that cover six or more models give you a fuller read on where you stand.

Prompt quality is something most buying guides don't address directly. Tracking the wrong prompts - ones that don't reflect how real users ask about your category - produces accurate data about the wrong questions. The CMI's 2026 benchmarks on AI referral traffic found that brands gaining ground in AI search are doing so through deliberate prompt strategy, not just presence. Some tools help you build the right prompt set; others leave that work entirely to you.

Pricing structure is worth thinking through carefully. Subscriptions make sense if you need daily tracking and will actually use the data at that cadence. Pay-as-you-go options have recently entered the market, and they suit teams whose tracking needs are periodic rather than continuous.

Finally, consider whether you need monitoring alone or monitoring plus action. Tools like AirOps position themselves as closed-loop systems that connect visibility data to content execution. If you want to go from "we're not showing up" to "here's the content we need to fix that" inside one platform, the tools that connect tracking to workflow earn their higher price point. If you have a content team that can act on the data separately, a standalone tracker costs less and covers the need.

Which tool is right for your situation

Enterprise team managing brand reputation in a regulated category: Profound. Teams already running Semrush or Ahrefs: use the AI visibility features baked into what you already pay for. Agencies managing multiple brand clients: Peec AI's multi-client architecture handles that cleanly. Small teams or solo founders who want periodic tracking without a subscription commitment: Contengi's LLM tracker is a solid option to look at. And if you're curious about why LLM citation positioning is so unforgiving, that context helps make sense of why tracking the data is worth doing.

Pay-as-you-go options have recently entered the market, and they make this space accessible to teams that subscription pricing never really served.

Frequently asked questions

How do LLM visibility tracking tools collect data?

Every tool runs the same core process: a defined set of prompts is sent via API to each major AI model - ChatGPT, Gemini, Perplexity, Claude, Grok, and Google's AI surfaces - and the responses are saved and analysed for brand mentions, citation sources, and sentiment. There is no proprietary feed or native analytics access from any of the major LLM platforms. The difference between tools is how many prompts they run, how frequently, and how cleanly they surface the results.

What is the difference between UI-based and API-based LLM tracking?

UI-based tools interact with the AI chatbot interfaces the way a real user would - which means they capture the experience as it appears, but responses can be slower and less consistent to collect at scale. API-based tools query LLMs directly via developer access, which is faster and more structured but can differ slightly from what a real user would see. Most dedicated tracking platforms use API access for efficiency; the key thing to check is whether the tool you're evaluating is transparent about which method it uses.

How many prompts do I need to track to get a useful picture?

It depends on your category. A niche B2B product might need 50-100 well-chosen prompts to cover the relevant query space. A broader consumer category could need 500 or more to capture meaningful share-of-voice data. The quality of your prompt selection matters more than raw volume - prompts that don't reflect how real users ask about your category produce accurate data about the wrong thing. Some tools help you build the prompt set; with others you're on your own.

Why is LLM tracking so expensive with most tools?

The cost driver is token volume. Sending one prompt to six models returns six lengthy responses, each of which needs to be stored and processed. At 500 prompts, a single tracking run costs $100-$150 in raw API and compute before any SaaS margin is added. Daily tracking at scale compounds that fast. Most subscription tools price for enterprise volumes, which is why smaller teams end up paying for headroom they never use. Pay-as-you-go is a more honest structure for periodic tracking needs.

Can I build my own LLM tracking tool instead of paying for one?

Yes - and if you're comfortable with basic automation setup, it's worth considering. The process isn't complicated: define your prompts, connect to the LLM APIs, save the responses, run analysis across them. At 744 prompts per cycle it costs around $100-$200 in API costs and takes a couple of hours to configure and schedule. A pay-as-you-go tracker delivers the same data without setup or maintenance time, if you'd rather not build it yourself.