How to Choose Between Profound, Evertune and AthenaHQ

Profound, Evertune and AthenaHQ solve different jobs. Profound sells enterprise-oriented AI visibility analytics, Evertune sells large-sample statistical brand-perception measurement, and AthenaHQ sells credit-based, action-oriented AEO workflows. The right pick depends on your team and budget, not on a single winner. Every price and methodology claim below was verified on the vendors' own pages on 26 September 2026.

What Profound, Evertune and AthenaHQ each solve

Start with a distinction worth drawing precisely: measurement platforms tell you how AI engines answer about your brand and category; execution platforms turn those answers into content and site work. All three products claim parts of both jobs, but each leans clearly one way.

Profound leans toward measurement and reporting for large organisations. Its public pages say little about plan structure, so the enterprise reading is partly our inference — drawn from the compliance and funding signals it publishes, detailed in the side-by-side section below — rather than a tier sheet the vendor spells out.

Evertune leans toward research. Its own FAQ frames the product around sampling depth and statistical significance (verified on 26 September 2026), which makes it a brand-perception measurement tool first, not a content workbench.

AthenaHQ leans toward execution. Its pricing page carries the tagline "Action on AI Search", and the product is organised around doing something with what the engines say, not only reporting it. One documented fact about the team: per the company's Y Combinator profile, verified on 26 September 2026, a co-founder was formerly a product manager on Google Search's information acquisition team and also worked on DeepMind's generative media team. That is a biography, not a quality guarantee — but it explains the product's search-native framing.

These three are not the whole market — Peec AI and a growing list of trackers compete for the same budgets — but they map the three postures a buyer chooses between: enterprise analytics, statistical research, and hands-on execution. We keep the comparison to them.

Engine coverage and measurement methodology

Which engines each platform tracks

AthenaHQ documents its coverage most plainly. Per its own answer pages, verified on 26 September 2026, it "monitors brand presence across the major generative AI engines, including ChatGPT, Perplexity, Gemini, and Claude, among others". "Among others" is the vendor's phrase; the page we verified does not name the rest.

Evertune states coverage as a count. Its FAQ, verified on 26 September 2026, says it samples "every prompt 100 times per model across 11+ AI models". The statement does not name the models, so we cannot enumerate the surfaces behind the figure. Ask for the current list in writing.

Profound is the open cell in this row. We could not tie its engine coverage to a citable vendor statement that survived verification for this article, so we do not reproduce a list, and the table below marks it as not verified. Treat any coverage list quoted elsewhere as a claim to confirm with the vendor.

How each platform samples answers

Sampling depth is what separates a signal from an anecdote. The same prompt, on the same engine, on the same day, can return different brands across runs; a tool that checks each prompt only occasionally reports variance as if it were movement.

Evertune is the one platform of the three with a sampling methodology we could verify: its FAQ states it samples every prompt 100 times per model, and presents the result as statistically significant visibility data (verified on 26 September 2026). The same vendor names a prompt panel it calls EverPanel. How that panel's prompts were recruited or validated is not documented on the pages we verified, so treat EverPanel as a vendor claim, not an audited dataset.

For Profound and AthenaHQ, we found no documented run counts or sampling methodology on the pages we verified on 26 September 2026. That absence is a finding in itself: if a vendor does not say how many times it runs each prompt, you cannot judge whether a reported change in visibility is real movement or noise. Put run counts on the demo agenda for both.

Pricing: what each vendor publishes and what it doesn't

AthenaHQ is the transparent one. Its pricing page, verified on 26 September 2026, lists a Starter tier at $295/month; the same page presents a free entry tier and prices usage in credits consumed as the platform collects AI responses. Credit pricing has a practical consequence: monthly cost scales with prompts multiplied by engines multiplied by runs, so a wide prompt set across several engines drains the pool faster than the sticker price suggests. Ask the vendor to model your intended volume before you commit.

Profound publishes no monthly figure we could verify. As of 26 September 2026, we could not tie any flat monthly price for Profound to a page the vendor controls. Flat figures do circulate; they contradict each other, and none we have seen traces to Profound's own pricing page, so we treat the real number as quote-only and list it below as a demo question rather than print one we cannot support.

Evertune we mark as not verified. We could not settle a citable, dated statement of what its plans cost in time for this article, so rather than repeat a figure we cannot source, we leave the cell open. Check its pricing page on the day you evaluate, and get the current figure confirmed in writing during the demo.

A "not verified" entry is a data point in its own right: a vendor that publishes its price lets you budget before a sales call; one that does not is telling you the price is negotiated.

The three platforms side by side, and which team each fits

The comparison table

"Not verified" means we could not tie the cell to a citable vendor statement on 26 September 2026; it is a question for your demo, not a fact about the product.

Two company-maturity facts are documented and matter for enterprise due diligence. Profound announced SOC 2 Type 2 certification on 10 June 2025 — in its own words, a commitment to "security, confidentiality, and availability". And on 15 September 2026 it announced a "$180 million Series D at a $1.8 billion valuation, co-led by Sequoia Capital and Kleiner Perkins" on its own newsroom. Funding figures for Profound have circulated in conflicting versions, which is why we cite the company's own dated announcement and nothing else.

One gap covers all three: none of the pages we verified documents multi-client or agency workspace structure, and none addresses data handling or GDPR posture. Both belong on your demo agenda in writing, not in the assumptions column.

If you are an enterprise brand team

Procurement and defensibility are your constraints. Profound's published signals — SOC 2 Type 2 certification, a large late-stage round — fit that context, so shortlist it, but make it earn the contract: require the engine list, run counts and actual price in writing before the pilot. If the job is brand-perception research rather than reporting, bring Evertune into the same process; of the three, it is the one with a sampling methodology we could verify.

If you are an agency managing several clients

None of the three documents multi-client workspaces on the pages we verified, so structure the demos around your reality: separate client views, per-client prompt sets, exportable reporting. Credit-based pricing needs per-client math, because prompts, engines and runs multiply across a portfolio. And decide in advance how to handle it when two tools report conflicting numbers — with different sampling depths, they will.

If you are an in-house marketer with a limited budget

AthenaHQ offers the one entry price we could verify: $295/month for Starter, verified on 26 September 2026 on its own pricing page, plus a free entry tier to test the workflow before paying. Start there for scoping. Treat measurement quality as the open question, since its sampling methodology is not documented on the pages we verified — pair the tool with your own fixed prompt set so you can sanity-check what it reports.

Where to start this week

A structured evaluation of these three fits in a working week if you prepare the inputs first.

• Define the job before any demo: decide whether you are buying measurement — how AI engines answer about you — or execution, changing those answers. The wrong platform type wastes a quarter.
• Freeze your method before you talk to anyone: lock a prompt set and enough runs to beat variance before comparing tools, plus a fixed competitor list. Vendors demo best on prompts they choose; you compare best on prompts you froze.
• Request the same sample from each vendor: your frozen prompts, your competitor list, a stated date range. Identical inputs are what makes the outputs comparable.
• Ask the undisclosed questions this comparison surfaced, in writing: Profound, the actual price and the current engine list; Evertune, the named models behind "11+" and how EverPanel's prompts were recruited; AthenaHQ, exactly how credits are consumed and whether multi-client structure exists; all three, data handling and GDPR posture.
• Baseline before you buy: follow a free DIY recipe for automated AI visibility checks so you can judge what a paid platform adds over what you can already measure yourself.

Before committing to an enterprise platform, run a free Namedrop scan to see where your brand stands today in ChatGPT, Perplexity, Gemini and Claude answers.

Sources

• AthenaHQ, Plans & Pricing | Action on AI Search, consulted 2026-09-26
• Evertune, Evertune FAQs, consulted 2026-09-26
• Y Combinator, AthenaHQ: Be the Answer in AI Search - AI SEO across SEO, GEO & AEO | Y Combinator, consulted 2026-09-26
• AthenaHQ, What is AthenaHQ's official AI search visibility platform?, consulted 2026-09-26
• Profound, Profound achieves SOC 2 Type 2 certification, consulted 2026-09-26
• Profound, Profound Raises $180M Series D at $1.8B Valuation to Build the AI Platform For Marketing Teams, consulted 2026-09-26