What Does AthenaHQ Actually Track?

Every AthenaHQ review on page one is published by a company selling a competing tracker. Rankability, Dageno, Radarkit, GetMint, Scalenut, Mentionable. Each one lands on the same verdict, which is that AthenaHQ is expensive and the reader should try the author's product instead.

That is where the incentive sits, so it is not surprising. What is surprising is that none of them do the arithmetic that would settle the argument either way.

We sell no platform. We run the measurement and the content work behind it for clients, so the only question we care about is whether the thing you are about to buy can carry the decision you are buying it for.

For AthenaHQ, answering that means reading one line on the pricing page and multiplying it out.

What does AthenaHQ actually track?

AthenaHQ tracks brand mentions, citations, share of voice, sentiment and competitor position across nine AI engines on its self-serve tier, including Claude, Grok, DeepSeek and Meta AI. It meters all of it in credits, where its pricing page states that one credit is one AI response. Engine count, prompt count and refresh cadence all spend from that one balance.

That last sentence is the whole post.

Everyone else sells you engines and prompts as separate lines. AthenaHQ sells you one jar, and every engine you switch on divides it.

The credit is the product, and it is priced per AI answer

Most vendors in this category meter something that does not map cleanly onto an AI answer: prompts per month, projects, seats, "queries." Comparing them means guessing at the conversion.

AthenaHQ removes the guess. It sells responses, which is the only unit that matters, and that makes it the easiest product in the bracket to price honestly.

One credit is one AI response, which fixes the cost per answer at $81.94 per thousand

The Starter tier is $295 a month for 3,600 credits. Divide one by the other and you get $0.0819 per answer, or $81.94 per thousand.

Extra credits are sold at $100 per 1,250, which is $80.00 per thousand. The vendor is being consistent with itself, which is more than most of this category manages.

That figure is the highest self-serve row in the bracket, by a wide margin

Against the numbers we have normalised across this market, Ahrefs Custom Prompts on the Scale package runs $10.00 per thousand answers and Otterly Premium $10.19. Peec Starter is $21.11. Profound Growth is $44.33 and Profound Starter $66.00.

AthenaHQ Starter sits above all of them at $81.94. Roughly eight times the cheapest row in the category, and about a quarter dearer than the previous ceiling.

Published tier names disagree with the vendor's own page, so confirm before modelling

AthenaHQ's page lists Essential free with 300 credits on five engines, Starter at $295 with 3,600 credits on nine engines, and a quoted Enterprise tier.

Third-party teardowns report a different ladder. Dageno describes Lite at $270 annual with 3,500 credits, Growth at $545 with 10,000 and Enterprise from $2,000. Trakkr's pricing breakdown says plainly that the exact annual charge and the API add-on price are not public.

Both cannot be current. If the Growth figure is real it prices at $54.50 per thousand, which is materially better than Starter and still above everyone except Profound Starter. Get the current ladder on the call.

What every AthenaHQ review asks:

• •What does it cost per month?
• •How many AI engines are included?
• •Does it show competitor share of voice?
• •Is there a free tier?

What the credit meter actually decides:

• •How many answers does one dollar buy?
• •How many prompts can I watch daily once nine engines are on?
• •Does my prompt set reach a number of answers that means anything?
• •Which of my three dials am I turning down to pay for the other two?

Every review answers the first list. The second list decides whether the subscription tells you anything.

6 things the credit meter decides that no AthenaHQ review mentions

None of these are defects. Each one follows from metering engines, prompts and cadence on a single balance, which is a design choice with consequences the feature list does not show.

Consequence #1: Nine engines is why your prompt count is thirteen

At daily cadence a Starter plan spends its 3,600 credits at 120 a day. Switch on all nine engines and one prompt costs nine credits per day, so the plan covers about 13 prompts.

Drop to three engines and the same $295 watches 40 prompts. Drop to one and it watches 120. The headline feature is the thing eating the prompt count.

Engine breadth is not a feature on AthenaHQ. It is a spending rate.

Consequence #2: Thirteen prompts is not a category, it is a shortlist

Thirteen prompts covers your brand name, two or three category terms and a handful of comparison queries. It does not cover a buying journey.

That is a defensible setup if you have already done the work of finding which thirteen questions matter. It is a poor setup for discovering questions you have not thought of, which is the job most teams actually buy a tracker to do. We worked through that gap in how to select prompts for LLM tracking.

Consequence #3: The free tier is a demo, not a pilot

Essential gives 300 credits a month across five engines. Run daily, that is 10 credits a day, or two prompts.

You can use it to confirm the product works and to see your brand in one or two answers. You cannot use it to establish a baseline, and any conclusion drawn from two prompts is a conclusion about two prompts.

Consequence #4: Adding an engine mid-quarter silently rewrites your history

On a per-engine pricing model, switching on Claude adds a line to the invoice. On a credit model, it adds nothing to the invoice and takes the credits out of everything else you were already watching.

Either your prompt set shrinks or your cadence slows. Both change what your trend line is measuring, halfway through the period you are trending. Nobody sends you an email about it.

Consequence #5: Sampling depth is capped by the calendar, not by your budget

This is the limit that applies to every vendor here, AthenaHQ included, and it is the one buyers consistently miss. Daily is the fastest cadence on offer anywhere in this bracket, so one prompt on one engine yields about 30 answers a month whatever you spend.

In July 2026 Ronald Sielinski published From Stochastic to Stable, which found rankings needed between 33 and 94 answers to settle across 30 platform-topic combinations, with three never settling at all. It is an unreviewed preprint and the exact numbers will not transfer to your category. The direction will. Thirty answers a month sits on the floor of that band, so a single month of daily data on any of these tools is the beginning of a reading rather than a result.

Consequence #6: The number was probably never the thing holding the program back

Our concluded CITE Index study ran 500 buyer prompts nightly through ChatGPT, Gemini and Google AI Mode for 63 days, collecting 90,132 AI answers across 10 consumer categories.

Across those 63 days the category leader flipped on only 18.7% of day pairs, and in four of the ten categories it never changed once. The average category leader held 78.2% of its own category's answers. The full corpus sits in the final report.

That is incumbency, not instrumentation. A nine-engine dashboard pointed at a standing that holds for weeks gives you a wider view of the same result.

What AthenaHQ sells that the cheaper trackers cannot

The cost-per-answer table is not a verdict on its own, and treating it as one is the mistake every competing review makes in the other direction. Fit is more useful to you than a ranking.

Nine engines on a self-serve plan is genuinely rare. Claude is Enterprise-only on Peec, as we covered in what Peec AI actually tracks, and it appears on neither half of Ahrefs Brand Radar at any price. Grok, DeepSeek and Meta AI are absent from most of the bracket entirely.

If your buyers are developers, researchers or anyone whose working day runs through Claude, the cheaper products cannot see them. A tool that costs eight times more per answer and can see your buyers beats a tool that cannot see them at any price.

The company is also a real one rather than a wrapper, which matters for a product you are trusting with a year of trend data. AthenaHQ was founded by former Google Search and DeepMind engineers and is backed by Y Combinator, with Coinbase, SoFi, Hearst and Twilio named as customers on its own site.

The geographic reporting is the third thing worth paying for. Visibility genuinely differs by market, and most of the cheaper tools treat location as an add-on or ignore it.

The right question is not whether AthenaHQ is expensive. It is whether the engines you are paying the premium for are the engines your buyers use.

Sizing an AthenaHQ plan before you buy

The diagnostic half is done. Here is the sequence we run with clients evaluating this product specifically.

Step 1: Name the decision the dashboard has to carry

Write one sentence naming a decision you will make differently based on what the tool reports. "Whether to fund review-site placement next quarter" is a decision. "Understanding our AI visibility" is not.

If no engine or prompt configuration changes that decision, the scope is the problem and no tier fixes it.

Step 2: Run your ten highest-intent prompts by hand across all nine engines

Before you pay for anything, open ChatGPT, Claude, Gemini, Google AI Mode, AI Overviews, Perplexity, Copilot, Grok and DeepSeek and ask the same ten shortlist-stage questions your buyers ask. Record where you appear, which competitors appear instead, and every domain cited.

Two hours of this tells you which engines carry your category. It is also the only way to find out whether the four engines you are paying AthenaHQ's premium for say anything different from the five the cheaper tools already cover.

Step 3: Multiply your prompt set by your engines by your cadence

Take your prompt count, multiply by the engines step 2 says matter, multiply by 30 for daily or roughly 4.3 for weekly. That product is your monthly credit requirement.

Twenty prompts on four engines at daily cadence is 2,400 credits, which fits inside Starter with room. The same twenty prompts on nine engines is 5,400, which does not, and needs about $160 of extra credits on top.

Step 4: Spend the difference on cadence rather than engines

This is the lever the credit model hands you and almost nobody uses. Decide which prompts genuinely need a daily read and drop the rest to weekly.

Dropping ten of twenty prompts to weekly on nine engines cuts the monthly requirement from 5,400 credits to about 3,090, which brings the whole set inside the base tier. We costed out that trade in how many prompts are enough.

Step 5: Establish your noise band before you set a single alert

Freeze the prompt set and run it for four to six weeks with no content or off-page changes, then record the spread. That spread is your category's noise band, and every alert threshold should sit outside it.

Skip this and a nine-engine daily refresh produces nine times as many arrows to explain in the Monday meeting, with no more information behind them.

Step 6: Fund the earned half from the same budget line

Whatever the licence costs, hold back a matching share for the work that puts you inside other people's pages: earned mentions, comparison placements, review-site position, community presence.

In our corpus, four of the twelve most-cited domains were brand-owned, which means eight were not. Reddit alone drew 14,698 citations and appeared in 13.6% of all 90,132 answers. Split the budget when you sign rather than after the first flat quarter. That split is the reason a managed GEO agency sits beside tooling rather than inside it.

Where AthenaHQ fits, by what each option constrains

Option	What it constrains	Right when
AthenaHQ Starter	Prompt count. Nine engines at daily cadence leaves about 13 prompts on $295	Your buyers sit on engines the cheaper tools cannot reach, and you already know which questions matter.
AthenaHQ Enterprise	Price, and it is quoted rather than published	You need persona targeting, claim review or many markets, and the credit allowance is negotiable.
Peec AI	Three of six engines included on every self-serve tier, with Claude held back for Enterprise	Your buyers cluster on three engines and you need unlimited seats.
Ahrefs Brand Radar	Engine mix. No Claude at any tier, and an Ahrefs subscription sits underneath	Cost per answer binds and you want a demand-weighted prompt corpus. What Brand Radar actually tracks prices both halves.
Profound	Price. Depth is negotiable only inside an Enterprise contract	Engine breadth or prompt-volume data informs your roadmap. What Profound measures works through the sampling question.
A managed program	Nothing, if scoped right. It costs a retainer	The measurement was never the bottleneck and nobody owns the weekly loop.

The six jobs to score any of these against sit in the AI visibility platform buyer's guide, and the rest of the field is mapped in our survey of GEO tooling for 2026.

FAQ

What is AthenaHQ?

AthenaHQ is an AI visibility platform that tracks how brands appear in answers from AI search engines and assistants. It reports brand mentions, citations, share of voice, sentiment and competitor position across nine engines on its self-serve tier, including ChatGPT, Claude, Gemini, Google AI Mode, AI Overviews, Perplexity, Copilot, Grok and DeepSeek. It was founded by former Google Search and DeepMind engineers and is backed by Y Combinator.

How much does AthenaHQ cost?

AthenaHQ's own page lists a free Essential tier with 300 credits on five engines, Starter at $295 a month for 3,600 credits on nine engines, and a quoted Enterprise tier. A credit is one AI response, which puts Starter at $81.94 per thousand answers. Extra credits run $100 per 1,250. Third-party reviews report a different ladder with Lite at $270 annual, Growth at $545 and Enterprise from $2,000, so confirm the current tiers on a call before modelling anything.

Is AthenaHQ worth it?

It depends entirely on which engines your buyers use. Per answer it is the most expensive self-serve product in this bracket at roughly eight times the cheapest row, and the credit model means nine engines at daily cadence leaves about 13 prompts on the base tier. Against that, nine engines at self-serve is genuinely unmatched, and Claude in particular is Enterprise-only or absent everywhere else. If your category lives on Claude, Grok or DeepSeek, the premium buys visibility no cheaper tool can show you.

What are the best AthenaHQ alternatives?

The names that come up most are Profound, Peec AI, Otterly, Scrunch AI, Semrush AI Visibility and Ahrefs Brand Radar. Compare them on cost per AI answer rather than on headline monthly price, because engine gating and per-model add-ons move the real figure by a wide margin. Then check which of them carries the engines your buyers actually use, because on Claude coverage most of the cheaper field drops out before price enters the conversation. Our Profound vs AthenaHQ comparison scores the closest of those matchups in detail.

Does AthenaHQ track every AI engine?

No, though it covers more than most. The self-serve tier lists nine engines and the vendor advertises eleven or more including Mistral at the Enterprise level. The free tier drops to five. Coverage is also not the same as depth here, because every engine you switch on spends from the same credit balance, so a nine-engine configuration watches roughly a third as many prompts as a three-engine one on the same money.

The bottom line

AthenaHQ is a better product than its reviews suggest and a more expensive one than its pricing page suggests, and both of those follow from the same fact. Selling AI answers by the credit is the most honest unit in this category. It is also the unit that makes the premium visible once you divide.

Do the multiplication in step 3 before you take a demo. Teams that skip it buy nine engines, discover in week three that thirteen prompts is not a category view, and spend the rest of the quarter arguing about a tier upgrade instead of about their position.

Then go do the work no licence covers. Nothing in the software writes the answer block, fixes the passage the model could not extract, or earns the third-party mention that puts you in the source pool. An AI visibility audit will show you which of those your gap sits in before you sign for a year of anything.