Comparison
LLM SEO tools: what they track, what they cost and how to choose one
LLM SEO tools ask ChatGPT, Gemini, Perplexity, Claude and other assistants the questions your buyers ask, then record whether your brand is named, recommended and cited. Most re-run a fixed prompt set on a schedule. The useful question before buying is not which tool has the most features, but how many answers you need each month — and whether you need a tool at all yet.
Disclosure: Llamat is our product. It appears in the table as a free one-off report, on the same terms as the other tools.
Method: every price, engine list and limit below comes from our AI visibility tools comparison, read from vendor pricing pages on 30 September 2026 and rechecked on 1 October 2026. We have not run these tools side by side, so nothing here is a test result.
What an LLM SEO tool does
LLM SEO is the work of getting large language models to name your brand and describe it correctly. An LLM SEO tool is the measuring side of that work. It sends your prompts to the engines, stores every answer and reads it for your brand and your competitors.
Most tools report the same core numbers: how often you are mentioned, how often you are recommended, where you appear in the answer and which pages are cited. Our guide to AI visibility defines each one. The tools differ less in what they count than in how much they let you count, on which engines and how often.
You will see the same products called LLM SEO trackers, LLM SEO checkers or LLM optimization tools. Two labels mean something else. “AI SEO tools” usually means writing assistants for ordinary SEO content. And “LLM optimization” is also a developer topic about making models run faster or cheaper.
What a tracking setup is made of
Every LLM SEO tool is configured from the same parts. Knowing them makes pricing pages easier to compare.
- Prompt set. The questions you track. Buyer questions that do not contain your brand name, such as “best [category] for [use case]”, show whether you get recommended. Questions with your name show how you are described.
- Engines. ChatGPT, Gemini, Perplexity, Claude, Copilot, Google AI Overviews and AI Mode. Entry plans often cover three or four; the rest can be add-ons or higher tiers.
- Runs. The same prompt can get a different answer the next time. A rate over many runs means more than any single answer.
- Cadence. How often the prompt set is re-run: daily, weekly or monthly. Daily shows noise as well as change.
- Market. Country and language. Answers can differ by location, and many entry plans cover one market.
- Collection method. Some tools call the engine's API, others collect answers through the app a person uses. SE Visible, for example, says it uses a browser rather than an API. Ask each vendor.
Two jobs: measure and improve
Measuring tells you which questions you lose and who wins them instead. Every tool in the table below does this.
Improving means turning those gaps into work: content suggestions, page audits, AI drafts or agents. Some tools add this on higher plans, and our AEO tools comparison groups them by that job. Treat improvement features as a to-do list, not a lever. The work that moves answers — clear pages, accurate facts, coverage on sites the engines cite — is described step by step in our LLM SEO guide.
How to choose an LLM SEO tool
Start from your own needs and check them against the plan you would actually buy, not the top tier.
- Engines on the entry plan. If your buyers use Claude, check that the plan includes it — several list it only as an add-on or on Enterprise.
- Answers per month. Work out prompts × engines × runs (next section) and compare it with the plan's prompts, checks or credits.
- Cadence. Weekly is often enough for a small prompt set. Pay for daily only if you will act on daily changes.
- Countries and languages. Every extra market multiplies the answers you need.
- Competitors and sources. Look for the brands named instead of you and the pages the engines cite. That is where the work starts.
- Getting your data out. Ask whether you can export raw answers or use an API. Charts are hard to audit; raw answers are not.
Work out your prompt budget
Pricing pages count in different units — prompts, checks or credits — so turn them all into answers per month. A worked example, with illustrative numbers: 25 buyer questions on three engines, checked daily, is 25 × 3 × 30 = 2,250 answers a month. Checked weekly, it is about 300.
Here is what 2,250 answers a month looks like on plans from the table. ZipTie charges $0.01 per prompt check per engine, so $22.50 of checks — though its presets start at $42.75. Ahrefs Brand Radar's $50 Custom Prompts package includes 2,500 checks, which just covers it. AthenaHQ's $295 Starter plan has 3,600 credits at 1 credit per AI response. Rankscale says a query to one engine typically uses 0.25 credits, so the 1,200 credits on its $99 Pro plan cover up to 4,800 answers.
Adding Claude changes the math on Ahrefs: its page says a Claude update uses 8 checks. Tracking the same 25 questions on Claude daily would take 6,000 checks on its own.
Prompt-based plans are simpler: HubSpot AEO includes 25 prompts on ChatGPT, Gemini and Perplexity for $50 a month, and Semrush's $199 Starter plan tracks 50 prompts daily. Check whether each prompt runs on every engine in the plan, and how often.