Comparison
Otterly.ai vs AthenaHQ
Cheap breadth against revenue attribution. A ten-times price gap between two tools that barely overlap in intent.
The short version
An unusual pairing, because these two barely overlap in intent.
Otterly.ai monitors six engines from $29/mo and produces GEO briefs. It is the cheap breadth option, well suited to consultants tracking a portfolio of brands.
AthenaHQ costs $295/mo and answers a different question: what did AI visibility earn us? Eight engines, drafting included, revenue attribution as the headline.
One is an instrument, the other a business case. Choosing between them means deciding whether your gap is monitoring or justification.
Where Otterly.ai wins
Coverage per dollar. Six engines at $29 is the best raw monitoring value in this set. AthenaHQ's eight engines cost ten times more, and two extra engines is not what you are paying for.
Portfolio economics. Cheap enough to run across many client brands, where AthenaHQ's per-account pricing would be prohibitive.
Predictable tiers. $29 for 15 prompts, $189 for 100, $489 for 400. Easy to forecast and to pass through to clients.
Actionable briefs. GEO briefs are more than dashboarding, and rare at this price.
Where AthenaHQ wins
Revenue attribution. The clearest answer to "was this worth it" available in the category. Otterly does not attempt it, and for organisations where marketing spend is defended in revenue terms this can be the only feature that matters.
Engine coverage. Eight against six, with no add-on fees.
In-platform drafting. Content generation included rather than left to you. Otterly stops at briefs, so the writing is yours either way.
Enterprise fit. Suited to organisations where the buyer sits close to finance and the reporting has to survive a board deck.
What neither does
Neither tests agent usability. No documented capability in either for having an agent attempt real tasks on your site and reporting where it fails. For AthenaHQ specifically this is an awkward gap: an agent that cannot complete a signup shows up in its attribution as an unexplained revenue shortfall rather than as a diagnosable fault.
Neither automates remediation. No scheduled fix drafting, no automatic page variants, no llms.txt serving documented for either. AthenaHQ drafts copy, which is a step further than Otterly's briefs, and still leaves publishing to you.
Neither publishes a factor model. Both report outcomes; neither attributes a citation loss to named causes.
Otterly has no crawler analytics at all. The sharpest gap in the pairing. The two most common causes of AEO failure — blocked crawlers and client-side-only rendering — are entirely invisible to it. AthenaHQ's coverage here is partial, which is thin but not nothing.
Our verdict
Otterly.ai for most teams choosing between these two.
Attribution is a compelling pitch and usually the wrong first purchase. It is most valuable once you have a programme running long enough to produce trend data worth attributing — and buying it before you have remediation capacity tends to produce a very well-measured flat line. Otterly covers the engines your buyers use, costs a tenth as much, and leaves budget for the content work that both tools will generate.
AthenaHQ when proving commercial value is the specific thing blocking your programme. If AEO spend has to be defended in revenue terms before it gets renewed, nothing Otterly does will win that argument and AthenaHQ might.
One caveat against Otterly that is worth weighing before you commit: with no crawler analytics, it cannot see the most likely reason you are invisible. Pair it with your own server-log checks, which are free — see Technical AEO for AI Crawlers.
Also worth reading
Both are assessed against the wider field in our comparison matrix. The method behind those assessments is in methodology.
Capability matrix
The same 23 capabilities we apply to every tool. A dash means we could not verify it in public documentation, not that it is absent.
| Capability | Otterly.ai | AthenaHQ |
|---|---|---|
| MeasurementWhat the tool can tell you about where you stand today. | ||
| Prompt-level trackingTrack individual buyer questions rather than only aggregate scores. | Yes | Yes |
| Citation and source analysisWhich domains AI engines cite when answering your prompts. | Yes | Yes |
| Competitor benchmarkingSide-by-side visibility against named rivals. | Yes | Yes |
| Share of voiceYour proportion of brand mentions within a topic. | Yes | Yes |
| Sentiment analysisWhether AI describes you positively, neutrally or negatively. | Partial | Yes |
| False-claim detectionFlags incorrect statements AI makes about your brand. | Not documented | Not documented |
| AI crawler analyticsServer-log view of which AI crawlers reached which pages. | No | Partial |
| AI referral trafficSessions arriving from AI answer surfaces. | Not documented | YesRevenue attribution on AI-driven traffic is the headline feature |
| DiagnosisWhether the tool explains the cause, not just the symptom. | ||
| Ranked citation-factor analysisAttributes a citation win or loss to specific, named factors. | No | Partial |
| AI readiness auditCrawlability and render checks for AI user agents. | Not documented | Not documented |
| Agent task testingAn AI agent attempts real tasks on your site and reports where it fails. | Not documented | Not documented |
| Topic-level visibilityRankings grouped by subject area rather than single prompts. | Partial | Yes |
| ActionWhether the tool closes the loop or hands you a to-do list. | ||
| Prioritised fix queueGaps converted into ordered, ready-to-apply changes. | PartialGEO briefs give teams something actionable, short of a managed queue | Partial |
| Content draftingGenerates draft copy aimed at winning citations. | PartialBrief-level guidance rather than full drafts | YesIn-platform drafting across eight engines, no add-ons |
| Scheduled autopilot draftsProduces fix drafts on a recurring cadence without being asked. | Not documented | Not documented |
| Automatic page variantsServes AI-optimised variants of live pages. | No | No |
| llms.txt generation and servingBuilds and hosts an llms.txt manifest for you. | Not documented | Not documented |
| Grounded AI assistantA chat analyst answering questions against your own scan data. | Not documented | Not documented |
| PlatformHow well the tool fits an existing stack. | ||
| Free planA no-cost tier, not just a time-limited trial. | Not documented | No |
| API accessProgrammatic read access to your data. | Partial | Not documented |
| MCP serverModel Context Protocol endpoint so AI coding agents can query the data directly. | Not documented | Not documented |
| White-label reportsClient-facing reports under your own brand. | Partial | Not documented |
| SSOSingle sign-on for team access control. | Not documented | Not documented |
✓ Documented~ Partial✗ Absent– Not documentedA dash means we could not verify the capability publicly, not that the tool lacks it. See methodology.
Frequently asked
- [1]Which is better value?
- Otterly on raw coverage per dollar — six engines at $29 against eight at $295. AthenaHQ is better value only if you specifically need revenue attribution, which nothing else in this pairing provides.
- [2]Does AthenaHQ justify ten times Otterly's price?
- Through attribution and in-platform drafting, yes, for the right buyer. On monitoring alone the gap is much narrower than the price suggests: six engines against eight. You are paying for the attribution model, not for coverage.
- [3]What do both miss?
- Agent task testing, automatic page variants, llms.txt serving and a published citation-factor model. Otterly also lacks crawler analytics entirely, which hides the most common cause of AEO failure.
- [4]Can I start with Otterly and move to AthenaHQ later?
- Yes, and for most teams that is the sensible sequence. Attribution is most useful once you have a programme producing trend data worth attributing, which means it is rarely the first purchase.