Attensira vs LLMrefs

Where does LLMrefs appear when a startup asks for an affordable tool?

We track "affordable AI visibility tool for startups" for our own site. On 3 October 2026 one ChatGPT answer recommended five tools: Otterly.ai, AthenaHQ's free tier, Lectern, Peec AI and LLMrefs, which it described as "multi-model tracking at a mid-range price". It advised a startup to track 20 to 50 important prompts manually before buying anything, to confirm AI visibility is a real growth channel. Attensira was not named.

We counted every answer to that question that names LLMrefs in the chart below. Attensira is in none of them: on price-led startup questions, engines recommend trackers, and we have not yet given them a reason to recommend a crew.

What does LLMrefs sell?

LLMrefs has the simplest pricing page in this category. On 4 October 2026 it listed one plan.

All in One, $79 a month, marked "Limited time only":

• 500 prompts tracked for brand mentions, sources and fan-out queries.
• Every AI search engine it supports at no extra fee, naming ChatGPT, Google AI Overviews, AI Mode, Gemini, Perplexity, Claude, Copilot and Meta AI.
• Unlimited projects under one subscription, and unlimited team members.
• Geo-targeting in more than 50 countries and 20 languages.
• Weekly reports, citation tracking, CSV export, API access and priority support.
• Bonus tools: an AI crawl checker, a Reddit finder and an llms.txt generator.
• A seven-day free trial, cancel any time.

For one flat monthly price, that is very wide coverage, and the bonus tools are useful even on their own.

Why would anyone pay more for fewer prompts?

Attensira's entry plan reads fewer prompts, on fewer engines, in fewer markets, and costs more. That needs explaining.

The explanation is that the two products spend your money on different things. LLMrefs spends it on watching. Attensira spends some of it on watching and the rest on doing. Attensira is software that does your AI-search visibility work: whatever it takes to win the answer. Its plan includes credits for an agent crew, and that crew works every morning.

The Daily win plan reads the questions you are losing and does up to three pieces of work toward them. On your own site, it rewrites the passage an answer should have quoted and delivers it as a draft pull request on your repository, or an unpublished draft in Webflow or WordPress. Off your site, it starts with the pages an engine already cites for your question that name a competitor and not you, and drafts the pitch or listing for your own mailbox. On Reddit and Hacker News, where LLMrefs's Reddit finder would show you the threads, the crew drafts the reply for you to post from your own account, with a disclosure line and at most one link, last.

If you will do that work yourself, or have someone who will, the extra prompts and engines are better value. If you will not, they are a larger view of a backlog.

How do the two handle measurement?

LLMrefs sends weekly reports across all 500 prompts. That is a good way to see the shape of your visibility across a market.

Attensira reads each tracked question once a day on each engine, and judges its own work. When a change ships, the crew reads the questions it targeted at +7, +14 and +30 days and compares a week of readings around each date with the week before the change. Every verdict states its sample size, and a change inside the noise is reported as "no change". Across five hundred prompts, a weekly report is the right instrument for spotting trends. For judging a specific fix, you want the fix's own questions read daily before and after, which only a product that shipped the fix can do.

How do the bills compare?

LLMrefs's bill is one flat price for tracking, and its page marks that price as limited time, so check it again before you renew. The price buys readings; acting on them is left to your team.

Attensira's bill is credits-first. The plan fee includes monitoring and a monthly allowance of credits for agent work; agents spend credits as they work; a monthly budget you set caps what the work can be charged; and committed work we do not deliver is credited back on the first of the month.

What are fan-out queries, and why do they matter?

LLMrefs tracks fan-out queries alongside mentions and sources. When an AI engine answers a question with web search, it often breaks the question into several smaller searches, and the pages that rank for those smaller searches become the pages it reads and cites. Knowing the fan-out tells you which searches you would need to rank for to be read at all.

That is useful intelligence, and it points at a particular kind of work: making sure a page of yours answers each of those sub-questions plainly, and that the third-party pages ranking for them mention you. It is exactly the work an agency would plan from the data.

Attensira's crew starts one step later, from the pages the engine actually cited in its answer. Those citations are the end of the fan-out: the pages that survived the engine's searching and made it into the answer. The crew works them in order, first the cited pages that name a competitor and not you, then the pages that rank above you but have not been cited yet, and only then proposes a new page of your own. Whichever data you start from, the job that follows is the same, and the question is who does it.

What does the first week look like?

With LLMrefs, you add prompts across projects and countries during the trial, and your weekly report shows where you appear and which sources engines lean on.

With Attensira, the crew reads your site and drafts your business profile on day one, takes its first reading of every question you track, and by the end of the week has ranked the answers you lose and put the first finished work in your inbox.

How did we check this page?

LLMrefs's facts come from its pricing page, fetched on 4 October 2026 and cited cell by cell below. Attensira's facts come from docs.attensira.com, with our prices, credits and engines rendered from the same source as our pricing page. The ChatGPT and Perplexity readings and our own mention counts come from our Attensira workspace for attensira.com, read through the Attensira MCP.

Related reading: Attensira vs Knowatoa, tracking prompts vs tracking keywords, and how the crew does the work.