LLMrefs Review: Features, Pricing, and Alternatives

What does LLMrefs do?

• Keyword-based tracking. You enter a keyword, such as "best women's running shoes", and LLMrefs generates related prompts, about 25 per keyword in its example, based on the kinds of conversations people have with chatbots.
• Aggregated results. Answers across those prompts are weighted and combined into one result per keyword: your share of voice, average position and number of cited URLs, beside competitors.
• Many engines. ChatGPT, ChatGPT Search, Google AI Overviews, AI Mode, Gemini, Perplexity, Claude, Grok, Copilot, Meta AI and DeepSeek, with no add-ons.
• Sources. A full list of the URLs engines cite for each keyword, filterable, with content gap insights.
• Global coverage. More than 50 countries and 20 languages.
• Extras. Prompt volume estimates, CSV export, an API and SEO utilities.

What does it cost?

At about 25 prompts per keyword, 500 prompts covers roughly 20 keywords. Our LLMrefs pricing page works through how far the allowance goes.

What does LLMrefs do well?

• Engine coverage for the price. Eleven engines with no add-ons at $79 is broad compared with tools that charge per engine.
• Keyword thinking. SEO teams can import existing keyword lists rather than writing prompts from scratch.
• Smoothing noise. Combining many prompts per keyword reduces the effect of a single odd answer.
• Seats and projects. Unlimited team members and projects suit agencies and larger teams.
• International work. Many countries and languages on one plan.

Where does it fall short?

• Weekly only. Reports are weekly; teams that need daily movement during launches will want something faster.
• Aggregation hides detail. A keyword-level share of voice blends many prompts; to see why you lose a particular buying question you need to dig into individual prompts and answers.
• Generated prompts. Prompts come from the keyword, so the set may not match the exact questions your buyers ask.
• Price uncertainty. The $79 price is marked as limited-time, so budget for a possible change.
• No drafted pages. It shows gaps and sources and includes an AI crawlability checker and an llms.txt generator, but it does not draft pages or comparison content.

When is a keyword score more useful than prompt-level results?

A keyword score answers "how visible are we for this topic overall?" It is useful for SEO reporting, for spotting topics where competitors dominate, and for comparing markets or languages. Prompt-level results answer "what happens when a buyer asks this exact question?" They are more useful when deciding what page to write or which source to approach, because the fix depends on the specific answer and the pages it cites. Many teams use both: a topic view to pick where to look, and individual answers to decide what to do. LLMrefs leans towards the first; its prompts and sources tabs let you drill into the second when you need to.

Who is LLMrefs best for?

SEO agencies and in-house SEO teams that already report by keyword, work in several countries or languages, and want broad engine coverage at a fixed monthly price. It is a weaker fit for teams that need daily data, want to choose every question themselves, or want drafted fixes in the same subscription.

How does LLMrefs compare with alternatives?

Which should you choose?

You think in keywords and want broad engine coverage cheaply: LLMrefs.

You want daily tracking of specific prompts: Otterly or Peec AI.

You want the widest engine list with flexible schedules: Rankscale.

You want keywords and AI in one low-cost tool: Keyword.com.

You want to read what the engines tell buyers on your key questions and get fixes: Kovmere. At the same monthly price as LLMrefs, it covers far fewer questions and engines, but each is a buyer question written for your market and asked three times on each engine in a baseline, with the answers and cited pages readable, the most-cited pages labelled by whether they mention you, and up to 12 fixes per billing period drafted from facts on your site.

How should you trial LLMrefs?

1. Pick five keywords you already track in SEO.
2. Open the generated prompts for each and check they resemble real buying questions.
3. Compare its sources list with what you see when you ask a few prompts yourself.
4. Check the countries and languages you need are set correctly.
5. Export the CSV to confirm the data fits your reporting.

What might an LLMrefs setup look like?

Take an illustrative outdoor gear retailer with a set of priority keywords in the US and UK. LLMrefs tracks them weekly across all engines, showing that its share of voice for "best hiking backpacks" is strong on ChatGPT and weak on AI Overviews, where review sites dominate citations. The team uses the sources list to target two review sites. For its five most valuable buying questions, it also reads answers one by one to see exactly how competitors are described.

What are Kovmere's limits compared with LLMrefs?

Kovmere tracks one market per site, at most 20 questions, on five engines rather than eleven, through engine APIs rather than consumer apps, and does not cover AI Mode, Copilot, Grok, Meta AI or DeepSeek. It has no API and no keyword import, and it sets the question basket for you: fixed on a Check, with up to 5 questions changeable per round on Pro after the first baseline. No tool can guarantee citations or recommendations.

What do people ask about LLMrefs?

Is LLMrefs accurate?

Its aggregation across many prompts per keyword smooths noise. Check a sample of prompts and sources against your own tests during the trial.

Can I write my own prompts in LLMrefs?

Its core method generates prompts from keywords. Ask the vendor how much control you have over the generated set.

Does LLMrefs track daily?

No. Reports are weekly.

Is the $79 price permanent?

It is marked as limited-time, so it may change.