AthenaHQ vs AirOps: Credits, Tasks and What Each Plan Actually Includes

AthenaHQ vs AirOps is a choice between two different units of work. AthenaHQ sells monitoring and actions priced in credits, where one credit is one AI response, with a free tier and a $295 Starter plan. AirOps sells a content and workflow system priced in tasks, with answer-engine insights on top, and no monthly price on its pricing page.

If you searched "athenahq vs airops", the useful comparison is not which logo is bigger. It is what each vendor measures, what its unit of billing buys, which engines it covers and who can reach the API. This page reads each vendor's public pricing and product pages as they stood in October 2026. It is a specification comparison, not a trial, so it reports no measured results. Prime AI Visibility publishes this site and sells a competing product, so it appears later in the article and is not ranked against the two vendors.

AthenaHQ vs AirOps: the short answer

1. Starting point. AthenaHQ starts from monitoring. Its pricing page lists prompt and response analysis, sources, competitor insights and content recommendations. AirOps starts from content production. Its pricing page lists tasks, brand kits, knowledge bases and workflows, with insights added above them.
2. Billing unit. AthenaHQ counts credits, and says one credit is one AI response. AirOps counts tasks, which it calls the currency used to complete actions such as generating content or extracting data. The two units cannot be converted into each other.
3. Engine coverage. AthenaHQ lists five engines on its free Essential plan and eleven models on Starter. AirOps lists ChatGPT insights only on Solo and insights across seven or more answer engines on Pro.
4. Price visibility. AthenaHQ publishes a free tier and a $295 monthly Starter price. AirOps lists what each plan contains but shows no monthly price for Solo or Pro on the pages read.
5. API access. AthenaHQ sells API access as a paid add-on on Starter and includes it on Enterprise. AirOps lists an "AirOps MCP + API" row in its feature table and documents an MCP server; confirm which plan includes the API.

Why the comparison is harder than it looks

Both products sit under the same search phrases, and both talk about AI search, but they start from opposite ends of the work. A team that mainly needs to know how often a brand appears and which sources answers cite is buying monitoring. A team that mainly needs to produce and refresh pages at volume is buying a production system. Most buying mistakes come from comparing one vendor's core against the other vendor's add-on.

An AI Visibility Platform answers a measurement question first: what did the engines say, and which sources did they use? A content system answers a production question: how do we make and update the pages those engines might use? The two questions connect, but a shortlist should say which one is the budget owner's real problem before the vendor demos start.

Measurement also has to stay separate from outcomes. A brand named in an answer, a website cited as a source, an explicit recommendation, a referral visit and a conversion are five different facts, and no tool on this page turns one into another. The Prime AI Visibility metrics page defines the first three, and Percepture's guide to AI search visibility metrics covers how a team can connect them to qualified demand and revenue. Percepture is a related company, and the guide is a framework, not a result from either vendor.

Method: how each vendor was read

Each vendor was read on the same five checks, in the same order: what the plan includes, what the billing unit buys, which engines are listed, whether an API or MCP server is documented, and which plan sizes are public. A field is marked "Not listed" when the vendor's own pages do not state it, which is a question for the demo, not proof that the feature is missing.

The sources are each vendor's pricing page and, for AirOps, its platform page, all read in October 2026. Google's SEO Starter Guide tells site owners that search engines need to find, understand and be able to show helpful pages, which is the same reason the content side matters to this comparison. Bob Generale wrote this comparison from those public pages. Treat every figure as a snapshot, because plans and limits change, and confirm anything you rely on in writing before you buy.

Plans and limits side by side

The first table covers the entry points and the billing unit.

	AthenaHQ	AirOps
Free entry	Essential: $25 free credit, 300 credits	Solo plan; free start offered
Paid price shown	Starter $295 a month	Not listed
Billing unit	Credits (1 credit = 1 AI response)	Tasks (workflow actions)

The second table covers plan sizes and engine coverage.

Plan	What the page lists	Engines
AthenaHQ Essential	300 credits, unlimited users	5 engines
AthenaHQ Starter	3,600 credits, unlimited topics, prompts and competitors	11 models
AirOps Solo	35,000 tasks, 1 brand kit, 3 knowledge bases	ChatGPT only
AirOps Pro	100,000 tasks, 1 brand kit, 5 knowledge bases	7+ engines

Enterprise on both vendors is custom: AthenaHQ lists custom credits, websites and access controls, while AirOps lists custom prompt, page and task limits. Credits and tasks are not comparable, so a larger number on one row says nothing about the other row.

AthenaHQ: monitoring first, with actions on top

AthenaHQ's pricing page describes three plans. Essential is free, with a $25 credit that buys 300 credits and covers ChatGPT, Perplexity, Google AI Overviews, Gemini and Copilot. It lists unlimited users, prompt and response analysis, sources and competitor insights, content recommendations and an Athena AI agent. For a small team that wants to see answers before paying, that is a real starting point.

Starter is $295 a month with 3,600 credits, where a credit is one AI response. The page lists visibility across eleven models, including ChatGPT, Perplexity, Google AI Overviews, AI Mode, Gemini, Claude, Copilot, Grok, DeepSeek, Meta AI and Mistral, with additional models available on request. It also lists unlimited topics, prompts and competitors, country-level support, CSV data export, integrations, on-page and off-page actions and a content optimization agent. A monthly toggle shows 17 percent off for annual billing.

Because a credit equals one response, the credit count is a ceiling on how many answers can be collected a month. A prompt tracked across several engines, countries or repeated runs uses several credits, so the headline "unlimited prompts" and the 3,600-credit allowance describe different limits. Ask how the vendor counts a repeated run before estimating a prompt list.

API access is the point to read carefully. The comparison table says API access is a paid add-on on Starter and included on Enterprise, and that extra credits are also paid add-ons. Enterprise adds SAML and OIDC sign-on, an audit log, multi-region and multi-language support, persona targeting, dashboards with support for Tableau, Power BI and Looker, a knowledge base with claim review and white-glove setup. The comparison table also lists Google Analytics and Search Console integration, which lets a team set AI visibility next to site traffic.

Fit: a team that wants broad engine coverage and a clear monthly price, with actions and a content agent in the same product. Limitation: the API and extra credits cost more on Starter, and the credit unit makes the monthly answer count the number to check.

AirOps: a content system with answer-engine insights

AirOps describes its platform as a growth system for the AI-first buyer journey, built around three steps: intelligence, action and measurement. Its pricing page lists three plans. Solo is for individuals building content for one brand or project, with ChatGPT insights only, 35,000 tasks for content production, one brand kit, three knowledge bases and single-user access. Pro is for small teams, with insights across seven or more answer engines, 100,000 tasks, one brand kit, five knowledge bases and unlimited seats. Enterprise is custom, with custom agent builds, unlimited knowledge bases and brand kits, a dedicated account manager and one-to-one onboarding.

A task is the billing unit for actions. The FAQ says tasks are used when generating content or extracting data, that not every step in a workflow counts, and that the number depends on step type and the data processed. Solo users who run out pay $0.025 per additional task, and the allowance resets at the start of each calendar month. That makes cost harder to forecast from a plan name alone, because a workflow that calls more models or data sources uses more tasks.

The feature table is grouped under Intelligence, Actions, Integrations and Organization. Intelligence rows include answer engines, agent analytics, competitor insights, sentiment and theme analysis, prompt volume, offsite insights, custom dashboards and conversion tracking. Actions rows include tasks, brand kits, knowledge bases, campaigns, playbooks and workflows, content review and quality scores, and human review and approvals. Integrations include CMS, SEO research, AEO research, project management and an "AirOps MCP + API" row. AirOps also documents an MCP server, which lets an assistant connect to the platform.

The platform page makes an argument worth testing in a demo: it says prompt-tracking tools count how often a brand appears for any buyer and that a broad visibility score can hide weakness with the customers a team needs to win. That is AirOps's position, not a measured finding. The practical version is to decide which buyers your question list represents before comparing any score.

Fit: a team that already runs content at volume and wants intelligence, agents and review steps in one workflow system. Limitation: the plans show no monthly price on the pages read, and cost depends on task use, so ask for a worked estimate on your own workflows.

Where AthenaHQ vs AirOps differ in practice

Three differences matter more than the feature lists.

• What a unit measures. An AthenaHQ credit is an observed answer. An AirOps task is a unit of work performed. One tells you what engines said, the other limits what the system can do for you.
• Who works in the product. AthenaHQ's free and Starter plans list unlimited users, so a wide team can read the same results. AirOps Solo is single-user, and Pro adds unlimited seats.
• How cost scales. AthenaHQ cost scales with answers collected and add-ons. AirOps cost scales with the volume and complexity of workflows.

Neither pricing page documents the same evidence trail, and neither states how a saved answer can be reopened later. For any score you plan to show a finance lead, ask each vendor whether the full answer text and its source links can be opened and exported.

Where Prime AI Visibility fits

Prime AI Visibility is an AI Brand Visibility Tool built around saved answers, and it is not ranked against AthenaHQ or AirOps. Its public pages describe saved answers for ChatGPT, Claude, Perplexity, Gemini and an AI Overview-style preview built from Google-grounded answers, with Brand named, Website cited, Source links and Explicit recommendation recorded as separate facts. Saved reports export as PDF or CSV at no credit cost. The pages read do not describe a public API, so a team that needs scheduled programmatic reads should treat that as a limitation.

The free Flash plan runs five questions across ChatGPT, Claude and Perplexity, up to 15 answers, one check a month. Starter at $69 a month adds Gemini and the AI Overview-style preview, with 25 questions per check and 200 shared credits. The Prime AI Visibility features page shows the saved-answer evidence, and the plan and credit table shows current limits. If you are still deciding what to measure, the AI visibility audit process comes first, and an AI visibility strategy tells you which pages and questions deserve production work.

A five-question check to run with either vendor

Run the same five questions, in the same order, with each vendor, and save the written answers.

1. What does one credit or one task buy, in plain terms? Ask for a worked example using ten of your own prompts.
2. Which engines and countries are included at my plan, and which are add-ons? Record the exact list.
3. What happens when I run out? AthenaHQ lists extra credits as an add-on and AirOps charges per extra task on Solo; ask what applies on your plan.
4. Can I open the full answer and its sources later? Aggregate scores without the underlying answer cannot be checked.
5. Which plan includes the API or MCP access I need? Get it in writing.

Score each answer as Stated, Partly stated or Not listed, never with an invented number. The result is a decision grid you can show a finance lead or a client.

An illustrative example: five buyer questions for a market research app

Assume a team is tracking Pulse Terminal, a public investing research app that describes grading stocks and ETFs and answering questions with sources under the answer. This is an illustrative public website, not a Prime client, not an endorsement and not a measured result, and the questions below are invented to show how a vertical question set is built.

Buyer question	What to record
Which app grades stocks on momentum and growth?	Brand named, sources
What tool explains why a stock has a given grade?	Website cited
Which research apps cite sources under answers?	Competitors present
How do I compare stock research apps for beginners?	Recommendation
Where can I screen ETFs by trend?	Brand named, sources

For each question, record the engines checked, save the full answer, then count the brand named and the website cited as separate facts. Next, ask whether each shortlisted tool can keep those rows. In a credit-based monitor, each engine and each rerun uses credits, so five questions across several engines uses several times five credits. In a task-based system, the question is whether the insight data is stored with its sources or only summarised. If an answer is kept in full, the team can rebuild this table in its own tools.

The same questions can be run by hand first. Our guide to AI visibility audits shows how to freeze the wording and the engines so a later run is comparable.

Decision rubric: which one fits which team

If you need to...	Look at...
Monitor many engines at a fixed price	Credits, engines, add-ons
Produce and refresh pages at volume	Tasks, workflows, review steps
Keep the full answer as evidence	Saved answers, export format

A short version of the logic follows. If your first problem is knowing what engines say about your brand, a monitor with a published price and a clear credit unit, such as AthenaHQ, gives you the simplest comparison. If your first problem is producing pages fast with review steps, a workflow system such as AirOps matches the job, with insights as a supporting layer. If you want to see answers and keep evidence at a low entry point, a saved-answer AI visibility tracker with a clear question count and export format may matter more than either unit.

What to do before you pay for either tool

A tool only helps if the questions behind it are right, so build the question set first. Write 20 to 30 buyer questions across problem, category, shortlist and comparison stages, name three to five competitors, and freeze both for a month. A free AI visibility checker run on five questions is a cheap way to test the wording first. Then decide which fields you will review each month: brand named, website cited and source links are a sensible start, with referral and conversion joined from your own analytics. For hands-on implementation work on the pages and sources those answers point to, Percepture offers generative engine optimization services, and Prime AI Visibility has a commercial relationship with Percepture.

Google's SEO Starter Guide is a useful test for the content side of any plan: helpful, findable pages with clear titles, descriptions and links come before any tool setting. A production system speeds up good pages and bad pages equally.

If you are leaving AirOps rather than comparing it with AthenaHQ, read our guide to an AirOps alternative by job and price.