# Lantad compared with Ahrefs

> Ahrefs Brand Radar tracks brand mentions and citations across AI platforms at very large scale; Lantad measures whether an AI crawler could read the page those citations would point at, and names the fix when it could not.

- Canonical page: https://lantad.co/vs/ahrefs
- This file: https://lantad.co/vs/ahrefs.md

## Key facts

- **Compared with:** Ahrefs
- **What they do:** Ahrefs Brand Radar tracks brand visibility across AI Overviews and AI Mode, ChatGPT, Perplexity, Microsoft Copilot, Gemini and Grok, and extends past the answer engines to YouTube, TikTok and Reddit. Its own page describes tracking brand mentions across AI answers, benchmarking against competitors in AI search, and finding AI citations. The dataset behind it is stated as over 474 million total monthly prompts, sourced from search-backed data rather than synthetic generation. Projects are unlimited, any domain can be analysed instantly with no setup, and custom prompts are included at 2,500 checks a month. Published pricing is 398 USD a month for selected platforms and 699 USD a month for all platforms, the latter adding YouTube, TikTok, Reddit, search demand and web visibility data.

Ahrefs also has genuine crawler-side visibility, and it is not a token feature. Bot Analytics, documented in their help centre at help.ahrefs.com, reports which bots visit a site, how often, and which pages they crawl, classified across 12 categories that include AI Assistant, AI Search and AI Crawler as separate buckets, with GPTBot and ClaudeBot named in its own example data. It collects server-side through a Cloudflare integration, either Logpush on Enterprise plans or a Cloudflare Worker that works on any tier including free, and it is free during beta. Separately, Ahrefs' own crawlers, documented at ahrefs.com/robot, respect robots.txt allow and disallow rules and crawl-delay, and Site Audit can execute JavaScript while crawling.
- **What Lantad does:** Lantad fetches one URL twice: as a plain HTTP request identifying as LantadBot with no JavaScript, the way most AI crawlers take a page, and again in a real browser. The two documents are diffed word by word, and the share of body text that survived the first fetch is Prose Parity. It carries 50 percent of the grade, with Access at 25, Structure at 15 and Schema at 10, and the methodology page publishes every weight rather than describing the score in adjectives.

Access is evaluated per crawler and from two independent sources: what robots.txt permits for each of 15 named tokens, matched the way RFC 9309 specifies, and what the live server actually returns to that user agent. Where those two disagree, the disagreement is itself the finding. Every defect the scan reports comes from a catalogue of 24 with a named remedy attached, plus stack guides for Next.js, React, Shopify, Framer and Lovable.

The answer-side surface is much smaller than Brand Radar's and it would be silly to claim otherwise. Paid plans track 100 to 250 prompts weekly across ten pages against up to 8 engines: 5 metered API engines, an always-on open-weight chain, a grounded engine, and Google AI Overviews captured from the public SERP through a vendor. There is no YouTube, no TikTok, no Reddit and no region or language dimension. Every figure here is read from the configuration the scheduler enforces rather than typed into the page.
- **Choose them when:** Choose Ahrefs when you need coverage and scale, and you have the budget for it. Brand Radar reaches surfaces almost nobody else does, YouTube and TikTok and Reddit alongside the answer engines, and its prompt dataset is an order of magnitude beyond anything Lantad samples. If your question is which sources AI systems cite in your category, how you benchmark against named competitors across many platforms, or where the conversation is happening outside the answer engines entirely, Brand Radar is built for that and Lantad is not remotely a substitute. Bot Analytics is also the better tool for the specific question of which AI crawlers are visiting your site and how often, which Lantad does not answer at all.
- **Choose Lantad when:** Choose Lantad when the question is what the crawler came away with. Bot Analytics can tell you GPTBot fetched 400 pages last week and cannot tell you that 380 of them arrived as an empty shell, because a log line records the request and not the usefulness of the response. Lantad fetches the page the way that crawler does, shows you the text that survived, scores the gap, and names the fix, and the scan is free with no signup, so the cheaper question gets settled before anyone buys a 398 USD a month subscription to watch the outcome.

## Lantad against Ahrefs, dimension by dimension

| Dimension | Ahrefs | Lantad |
| --- | --- | --- |
| Primary question answered | What are AI platforms saying about my brand, which sources do they cite, and how do I compare | Did the AI crawler receive my page, and if not, what exactly stopped it |
| Surfaces covered | AI Overviews and AI Mode, ChatGPT, Perplexity, Copilot, Gemini, Grok, plus YouTube, TikTok and Reddit | Crawler access for 15 named bots; prompt tracking across up to 8 answer engines, with no social or video surfaces |
| Prompt scale | Over 474 million monthly prompts in the dataset, search-backed rather than synthetic, plus 2,500 custom prompt checks a month | 100 to 250 prompts a week derived from your own pages, roughly 430 to 1,080 checks a month |
| Crawler-side visibility | Yes: Bot Analytics reports which bots visited, how often and which pages, across 12 categories including AI Crawler, collected server-side through Cloudflare | Different question: not who visited, but what a visitor would have received, measured by fetching the page as a crawler and diffing it against the rendered view |
| Readability scoring | Not the product's focus; Site Audit can execute JavaScript while crawling, which shows the rendered view rather than the gap between the two | The core of it: raw-versus-rendered diff scored 0 to 100 with a per-check breakdown |
| Fixing what it finds | Reports and benchmarks; remediation is yours | 24 catalogued defects each with a named remedy, plus stack guides and paid Deep Audit and Fix Sprint work |
| Published price | 398 USD a month for selected platforms, 699 USD a month for all platforms | Free scan with no signup, then published plans at 59, 199 and 399 USD a month, none of which can be bought yet: Lantad is pre-revenue and the pricing page says so |

## Which to choose

| Choose | When |
| --- | --- |
| Ahrefs | Choose Ahrefs when you need coverage and scale, and you have the budget for it. Brand Radar reaches surfaces almost nobody else does, YouTube and TikTok and Reddit alongside the answer engines, and its prompt dataset is an order of magnitude beyond anything Lantad samples. If your question is which sources AI systems cite in your category, how you benchmark against named competitors across many platforms, or where the conversation is happening outside the answer engines entirely, Brand Radar is built for that and Lantad is not remotely a substitute. Bot Analytics is also the better tool for the specific question of which AI crawlers are visiting your site and how often, which Lantad does not answer at all. |
| Lantad | Choose Lantad when the question is what the crawler came away with. Bot Analytics can tell you GPTBot fetched 400 pages last week and cannot tell you that 380 of them arrived as an empty shell, because a log line records the request and not the usefulness of the response. Lantad fetches the page the way that crawler does, shows you the text that survived, scores the gap, and names the fix, and the scan is free with no signup, so the cheaper question gets settled before anyone buys a 398 USD a month subscription to watch the outcome. |

## The short version

Ahrefs came to AI search from the biggest crawl dataset in the industry, and it shows. Brand Radar, at ahrefs.com/brand-radar, covers more surfaces than most dedicated AEO tools, including YouTube, TikTok and Reddit alongside the answer engines, and it is backed by a prompt database Ahrefs puts at over 474 million monthly prompts drawn from search-backed sources rather than synthesised. On coverage and scale, this is not a comparison Lantad wins, and this page is not going to pretend it is.

The gap is elsewhere. Brand Radar answers what AI systems say and which sources they cite. Lantad answers whether your page survives the fetch that has to happen before either of those is possible. Ahrefs also ships Bot Analytics, which is real crawler-side visibility and gets its own section below rather than a footnote.

## Two different things called AI visibility

The phrase covers two measurements that are related but not interchangeable, and most of the confusion in this category comes from using one word for both.

The first is presence in the output: how often AI systems mention you, which sources they cite, how you compare to rivals. That is Brand Radar's job and it does it across more surfaces than almost anyone.

The second is whether the input was readable: whether the crawler that feeds those systems received your content in the first place. That is Lantad's job, and it sits earlier in the chain. Ranking is decided after a fetch; citation is decided during retrieval. A page that a crawler could not parse cannot be cited no matter how good the content is, and no amount of output monitoring will say why.

The two are complementary and the ordering matters. [AI visibility](https://lantad.co/glossary/ai-visibility) covers the term itself, and [answer engine optimization](https://lantad.co/glossary/aeo) sets out the whole chain from crawl to citation for anyone new to it.

## Bot Analytics, and what a log line cannot tell you

This is the closest either product comes to the other, so it deserves precision rather than a dismissal.

Bot Analytics is real crawler-side data, collected server-side through a Cloudflare integration rather than a JavaScript pixel, which means it sees bots that never execute a script. It classifies across 12 categories, with AI Assistant, AI Search and AI Crawler kept separate, and names GPTBot and ClaudeBot in its own examples. The underlying data path is Cloudflare's, and [Cloudflare's own AI Crawl Control documentation](https://developers.cloudflare.com/ai-crawl-control/) describes the same visibility it exposes directly. It reports which bots visited, how often, and which pages they crawled. If your question is whether the AI crawlers are showing up at all, that is a better answer than anything Lantad produces, and Lantad does not produce it.

What a log line records is a request and a status code. It does not record whether the bytes returned contained your article. A crawler that fetches a client-rendered page gets a 200, a fast response, an entry in the log, and a document with a navigation bar and an empty div where the content should be. In Bot Analytics that is a healthy row. In a Lantad scan it is a Prose Parity failure with the missing text shown to you.

So the two answer adjacent questions: who came, and what they got. [What GPTBot sees](https://lantad.co/tools/what-gptbot-sees) demonstrates the second for free on any URL, and [AI crawler](https://lantad.co/glossary/ai-crawler) explains what these bots are and how they differ from search crawlers.

## Their llms.txt study, and why we still ship the checker

Ahrefs published research in 2026 that partly undercuts a tool on this site, and the honest thing is to link it rather than hope nobody finds it.

Their llms.txt study, published on 15 June 2026 at ahrefs.com/blog/llmstxt-study, analysed 137,210 domains with traffic in May 2026 and reported that 28 percent published an llms.txt file, that 97 percent of those files received zero traffic in that month with nothing fetching them at all, that 96 percent of the requests which did arrive came from bots, and that 77 percent of those bots were not AI tools. Their own caveat is that the sample skews more technical and SEO-aware than the web at large. It is good work and the conclusion is uncomfortable for anyone selling llms.txt as a lever.

Lantad still ships [an llms.txt checker](https://lantad.co/tools/llms-txt), and the reason is narrow. The file is cheap to write, it is a documented convention at [llmstxt.org](https://llmstxt.org/), and knowing whether yours exists and parses costs nothing. What we do not do is score it as though it were causal, or imply that publishing one produces citations. It contributes nothing to the 50 percent Prose Parity weight and nothing to Access, which are the two dimensions that actually decide whether a crawler received your text. [The llms.txt glossary entry](https://lantad.co/glossary/llms-txt) states the same position.

If a vendor tells you llms.txt is the fix for AI visibility, that Ahrefs study is the number to put in front of them.

## 474 million prompts against ten pages

On scale this is not close, and pretending otherwise would insult the reader.

Brand Radar sits on a dataset Ahrefs puts at over 474 million monthly prompts drawn from search-backed sources, with 2,500 custom prompt checks a month included. Lantad tracks 100 to 250 prompts a week across ten pages, which is roughly 430 to 1,080 checks a month, and the prompts are derived from your own pages rather than drawn from a market-wide corpus.

Those are different instruments. A large prompt corpus tells you what a market asks and where the attention is, which is a research question. A small set derived from your own pages tells you whether the specific claims on those pages come back in answers, which is a page question. Lantad's is deliberately the smaller one, because the prompt tracker exists to give evidence about pages the scanner has already measured, not to survey a category.

One thing applies to both and to every vendor in this space: an answer sampled twice can differ twice, so a share of voice from any sample carries uncertainty that a headline percentage hides. Lantad publishes the sample size next to the figure. [The prompt finder](https://lantad.co/prompts) shows which questions a given page could answer, free, before any of this becomes a purchase decision.

## What each one costs

Brand Radar publishes 398 USD a month for selected platforms and 699 USD a month for all platforms, the latter including YouTube, TikTok, Reddit, search demand and web visibility data. That is a serious budget line, and for a brand operating across those surfaces it may well be the right one.

Lantad's scan is free, needs no signup, and returns the full report and the fix list before any form. The published plans are 59, 199 and 399 USD a month. None of them can be bought today: Lantad is pre-revenue and [the pricing page](https://lantad.co/pricing) says so in those words. The scanner, the report, the fix list and the free tools are live and working.

The point is not that one is cheaper. It is that the readability question can be settled at zero cost in a few minutes, and settling it first changes what the expensive subscription is worth. Monitoring a site whose pages arrive empty produces an accurate, well-designed report of nothing happening.

## Running both, in the order that works

For a brand large enough to buy Brand Radar, the sensible sequence is not either-or.

[Run the free scan](https://lantad.co/) on the pages you most want cited, and read two things: the Prose Parity share, and whether the robots file and the live server agree per crawler. If text is missing without JavaScript, that is the finding and nothing downstream can compensate for it.

Fix it. [The fix list](https://lantad.co/fix) names the remedy per defect and the stack guides cover the common causes. Then re-scan the same URL, because this is the one before-and-after in the chain that is genuinely measurable on both sides.

Then let Brand Radar do what it is good at: watch the surfaces, track citations, benchmark the competitors. Bot Analytics alongside it will tell you whether crawler traffic changed after the fix, which is a genuinely useful pairing with a Lantad scan, since one shows who arrived and the other shows what they would have received.

What no tool here can promise is the citation itself. That is decided inside systems nobody outside the model vendors can read, which is why [the methodology page](https://lantad.co/methodology) separates what Lantad measures from what it samples, and why this page does not carry an outcome claim.

## How to check any of this yourself, for nothing

Everything above about the page layer is testable without an account, which is the point: an argument about evidence should not have to be taken on trust.

[The robots.txt tester](https://lantad.co/tools/robots-txt-tester) shows which AI crawlers your robots file allows, token by token. [What GPTBot sees](https://lantad.co/tools/what-gptbot-sees) fetches your page as a no-JavaScript crawler and shows what survived. [The llms.txt checker](https://lantad.co/tools/llms-txt) reports whether the file exists and parses, with the Ahrefs finding above in mind. [The AI crawler list](https://lantad.co/tools/ai-crawlers) names every bot, its owner and its purpose.

For the wider picture, [the crawlability study](https://lantad.co/research/crawlability-study) reports what these failures look like across a population of sites rather than a single URL, and [the blog](https://lantad.co/blog) works through individual findings in detail.

The other comparisons on this site cover [Semrush](https://lantad.co/vs/semrush), which is the closest analogue to Ahrefs here, plus [Profound](https://lantad.co/vs/profound), [Peec AI](https://lantad.co/vs/peec) and [Otterly](https://lantad.co/vs/otterly).

## Questions and answers

**Is Lantad an Ahrefs alternative?**

No. Ahrefs is a large SEO platform whose Brand Radar product tracks brand mentions and citations across AI platforms, YouTube, TikTok and Reddit at a scale Lantad does not approach, alongside backlink, keyword and site audit tooling Lantad does not have. Lantad measures one upstream thing: whether AI crawlers can reach and read a page, scored and fixed. Nobody should drop Ahrefs for Lantad.

**Ahrefs has Bot Analytics. Does that not already show AI crawler problems?**

It shows a real and different thing. Bot Analytics reports which bots visited, how often and which pages, from Cloudflare server-side data across 12 categories including AI Crawler, and for the question of whether AI crawlers are visiting at all it is better than anything Lantad offers. What it cannot show is what the crawler received, because a log records a request and a status code. A client-rendered page returns a healthy 200 with no body text in it, which reads as a normal row in Bot Analytics and as a Prose Parity failure in a Lantad scan.

**Ahrefs found that 97 percent of llms.txt files are never read. Why does Lantad have an llms.txt checker?**

Because the file is cheap and knowing whether yours parses costs nothing, not because it is a lever. That study is good work and it is linked on this page rather than avoided. Lantad does not score llms.txt as causal: it contributes nothing to Prose Parity or Access, the two dimensions that carry 75 percent of the grade and actually decide whether a crawler received your text. Anyone selling llms.txt as the fix for AI visibility should be shown those numbers.

**Which one should I buy first?**

Neither, first. Run the free Lantad scan, which needs no signup, and find out whether crawlers receive your pages. If they do not, that is the thing to fix, and monitoring bought before the fix will accurately report an absence you already know about. If they do, the readability question is closed and Brand Radar's coverage is the more useful spend.

---

Lantad measures whether AI crawlers can actually read a page: it fetches as a non-rendering
crawler, renders as a browser, and reports the gap. Free scan, one URL, no signup.

Method and weights: https://lantad.co/methodology | All pages as markdown: https://lantad.co/md | Crawler policy: https://lantad.co/bot