How to evaluate an AI visibility platform before you buy
Evaluating an AI visibility platform starts with the room the number will be quoted in, not with the feature list. Decide what the figure has to survive, then check five things in this order: how deep each prompt is sampled, which engines the plan itself covers, whether a source an engine used is told apart from one it named, what the vendor meters, and whether the record explains a movement or only reports one. A trial that skips those runs on impressions.
We publish this page and sell an answer engine optimization platform. GetXEO publishes no per prompt sampling figure, which is the first thing this page tells a buyer to ask for. Every figure was read on the vendor pages on 29 August 2026.
In short
The questions this page answers, and the short answers.
- How should a team evaluate an AI visibility platform?
- Work backward from the room. If the number will be challenged, sampling depth decides everything and a shallow tool is disqualified whatever else it does. If the number is steering a content plan, coverage and source detail matter more than depth. The feature list is the last thing to read, not the first.
- What should a buyer check first on an AI visibility platform?
- The sampling figure, because almost nothing else survives without it. Profound publishes about ninety checks per prompt a month and Peec AI runs each prompt every twenty four hours on every selected model, which converts to roughly thirty. Three of the ten publish nothing a buyer can convert into a rate at all.
- How long should an AI visibility trial run?
- Long enough to see the same prompt move, which in practice means at least two weeks on a fixed prompt set. A trial shorter than that reports one reading per tool, and one reading cannot be told apart from noise on any platform in this category.
- What do AI visibility platform prices leave out?
- The unit. This field meters prompts, responses, checks, credits and answers a day, so two headline prices are rarely the same purchase. Location and model settings multiply several of those meters, which means a quote can double before anything is published.
What the number has to survive
An evaluation starts with the room it enters.
Nearly every bad tracking purchase starts the same way, with a demo that looked good and a question nobody asked first: who is going to argue with this number, and about what.
There are three common rooms, and they lead to different products. A figure going into a board pack will be challenged, so the evidence behind it matters more than the interface. A figure steering an editorial calendar has to point at a page, so the source record matters more than the depth. A figure justifying budget across several markets has to hold up per market, so coverage and location rules decide it.
Writing that answer down before the first demo changes the shortlist immediately. It also gives an evaluation a way to end, which most evaluations in this category lack.
Five questions that separate this field
The questions that actually split one vendor from another.
Most feature lists in this category converge. These five do not, and each has a published answer somewhere on a vendor site.
| Ask | The question | Why it separates |
|---|---|---|
| 1 | How many times is one prompt run before a figure is drawn? | Model answers move between runs, so depth decides whether a trend exists |
| 2 | Which engines does this plan cover, rather than this product? | Coverage is a plan row nearly everywhere, and entry tiers are narrow |
| 3 | Is a source an engine used told apart from one it named? | The split decides whether a page is earning attribution or feeding a rival |
| 4 | What exactly does the meter count? | Five different units are in use, so headline prices do not compare |
| 5 | Does the record explain a move, or only report one? | A figure with no explanation produces meetings rather than decisions |
Question one is the hardest to get answered and the most decisive. Profound publishes a hundred prompts and about nine thousand responses a month on its main paid plan, which is a checkable claim. Three vendors publish nothing a buyer can convert into a rate: Conductor, Scrunch AI and AirOps. Our own weekly rhythm converts to about four scheduled runs a month, which is the thinnest rate here and belongs in the same conversation.
Read the meter before you read the price
Five different units, so no two prices compare.
The most expensive mistake available here is comparing two headline prices that count different things.
- Prompts and responses. Profound counts both, which is what turns a depth claim into arithmetic a buyer can repeat
- Prompts, models and countries. Peec AI includes three models per plan and caps countries at one to three below enterprise, so coverage and volume move the same invoice
- Checks. Ahrefs counts one prompt on one platform in one location as one check, which multiplies in configuration rather than in output
- Credits. AthenaHQ counts one AI response as a credit, weights heavier models at five and multiplies by four with fan out on
- Answers a day. Writesonic tracks fifty, three hundred and six hundred answers daily across its three brand plans, which reads generously until the engine list is fixed
Two vendors publish no price at all, and one more sits inside a larger suite, so the cheapest Semrush plan carries no AI visibility while the toolkit is sold on its own at ninety nine dollars a month with twenty five tracked prompts. Build the quote in the vendor unit and then convert, rather than the other way around.
Run one trial that produces comparable numbers
One prompt set, one period, every tool measured together.
Trials in this category usually run one tool at a time, which produces figures that cannot be set beside each other. A comparable trial is not much more work.
- Fix the prompt set first. Twenty to forty real buyer questions, written once and used unchanged in every tool, because a different prompt set is a different measurement
- Fix the period. Two weeks minimum, and the same two weeks where the calendar allows, since engines change underneath everybody at the same time
- Record the settings, not just the score. Engines, locations and models per tool, because a score with no settings beside it cannot be explained later
- Ask each vendor for the run count. The figure behind the figure, in writing, and treat a refusal as information rather than as an obstacle
- Publish nothing new during it. A trial that overlaps a content push measures the push, and every tool will disagree about how much
Expect the tools to disagree, and expect the disagreement to be larger than the differences between competitors inside any one tool. That is the property being tested. A vendor whose figure sits far from the others should be able to explain why in terms of sampling, coverage or definitions.
Where an evaluation should stop
The limits of what any trial can tell you.
A trial can settle which platform reports the most defensible number. It cannot settle three things, and pretending otherwise is how evaluations run for months.
It cannot tell a team whether the movement it sees was caused by anything the team did. Engines change retrieval on their own schedule, and every brand in a category moves together when that happens. Answers vary between runs of the same prompt on top of that, which is why two weeks is a floor rather than a target.
It cannot rank vendors on the part of the work that follows the number. Research on what earns a citation describes causes, and a dashboard records outcomes. Tracking is the fifth link in a chain that starts with knowing which questions buyers actually ask and ends with a page an engine can lift cleanly, and a trial only exercises the last link.
GetXEO sells into that chain and it would suit us for the chain to be the whole argument, so the honest version is narrower. Buy the tracking on the tracking. Just do not expect the purchase to answer what to publish on Monday, because nothing in this category does, GetXEO included.
Frequently asked questions
Longer tail questions that did not need a section of their own.
What is sampling depth in AI visibility tracking?
The number of times a platform runs one prompt before it reports a figure. Depth exists because answer engines are not deterministic, so a single run is one draw from a distribution. A platform that publishes prompts and responses separately is stating its depth. One that publishes only a refresh rate is not.
Should an AI visibility trial cover several engines or one deeply?
Cover several when nobody knows yet where the brand is weak, because the finding is which engine ignores it. Go deep on one when the engine is already known and the figure will be challenged. Most entry plans force this choice, since engine lists widen further up the price card.
How many prompts belong in an AI visibility trial?
Twenty to forty real buyer questions is enough to see a pattern without turning setup into a project. Fewer than twenty leaves every figure resting on one or two prompts that may move for their own reasons, and more than forty mostly adds configuration work that has to be repeated in every tool being tested.
Should an AI visibility trial run during a content push?
No, and the reason is that the two cannot be separated afterward. A trial overlapping a publishing campaign measures the campaign, and every tool will disagree about how much. Hold the pages still for the trial window, then run the campaign against whichever tool was chosen.
Sources
The 13 records behind every external claim on this page.
All were published or last updated within the past twelve months. A competitor page appears only as a record of that vendor’s own published terms.
- Gond and others at Microsoft Research, enabling determinism in LLM inference, January 2026
- Machine Relations, AI search citation factors research
- Profound, the published pricing page and plan comparison, read 29 August 2026
- Peec AI, the published pricing page and plan comparison, read in a browser 29 August 2026
- Ahrefs, the published Custom Prompts page and plan quotas, read 29 August 2026
- AthenaHQ, the published credit calculator, read 29 August 2026
- Writesonic, the published pricing page and plan comparison, read 29 August 2026
- Semrush, the published pricing page and plan comparison, read 29 August 2026
- Semrush, the published AI Visibility Toolkit knowledge base entry, read 29 August 2026
- Conductor, the published pricing and plan comparison, read 29 August 2026
- Scrunch AI, the published pricing page, read in a browser 29 August 2026
- AirOps, the published pricing page and plan comparison, read 29 August 2026
- GetXEO, the published dashboard feature page, read 29 August 2026
Read next
The rest of this cluster, in the order it makes sense to read.