Evertune alternatives: it is the one vendor here that publishes how many times it samples a prompt, and that sampling is a paid upgrade

Read on 20 October 2026, the Evertune pricing table carries a row that nobody else in this category publishes: statistical significance from day one, one hundred samples per prompt. It also shows that row with a dash against the entry plan and a tick against the two above it. That is the single most useful sentence on any vendor pricing page in this market, because repeat sampling is the thing that decides whether a reported number is a measurement or a snapshot, and Evertune has priced it as a tier. For anybody searching Evertune alternatives, the question underneath is almost always that row: how many readings is the number you are about to pay for built from.

We sell an AI visibility tracker. Our own practice is in the table below on the same criterion, and Evertune beats us on it.

Why a sample count decides the whole purchase

An assistant answer is generated, not retrieved. Ask the same question twice in the same hour and the names in the answer can move. So a tracker that asks once a month and reports a figure is reporting one draw from a distribution, and a tracker that asks the same question many times is reporting the shape of it. Both products print a percentage on a dashboard and the two percentages mean different things.

Almost nobody in this category publishes the draw count. Evertune does, and states the tier it begins on. Depra publishes the next most specific thing we have found, a confidence floor stating that a mention below fifty per cent confidence never counts towards a score, along with its cadence per plan. Our own methodology page publishes the collection route for each of the five surfaces we name and the cadence behind every plan, and it does not publish a per prompt sample count, which is the gap on this page's own criterion and we are naming it rather than working around it.

The criterion, applied to everybody including us

One criterion, repeatable: does the vendor publish, in a document you can fetch, how many times it asks a question before it reports a number.

read 20 Oct 2026	what it publishes about repeat sampling
Evertune	one hundred samples per prompt, stated in the plan table, with the row ticked on the middle and top plans and dashed on the entry plan
Depra AI	a fifty per cent confidence floor below which a mention does not score, and the cadence of each plan
AI Knows Us, ours	the cadence of every plan and the collection route for each of the five surfaces, and no per prompt sample count
Rankscale	scheduling frequency from hourly to monthly on every plan, and a credit cost per engine query, which bounds the draws without naming them
Otterly.AI	daily tracking frequency on every tier
Peec AI	daily tracking on the monthly plans, daily or weekly on the top plan, and a stated formula of prompts multiplied by active models multiplied by frequency
Conductor	an annual AI search credit allowance, and no cadence or sample count on the page
Brandlight	nothing on the three documents we read

Evertune is first on this criterion and it is first by a distance. Peec is the most useful second place, because publishing the multiplication, prompts by models by frequency, lets a buyer calculate the draw count even where the vendor does not state it. Our own row is honest and it is not the top of the column.

What else the Evertune plan table publishes, and where the lines fall

Fetched under a Chrome on Windows agent and a Safari on macOS agent, both HTTP 200 with identical bodies. The limits, with no figure reproduced here, are the part a buyer can plan against.

limit, as published	Starter	Pro	Enterprise
prompt credits a month	5,000	100,000	custom
models tracked	5	11	11
one hundred samples per prompt	no	yes	yes
daily monitoring	yes	yes	yes
competitor benchmarking	yes	yes	yes
content generation	no	25 articles a month	unlimited
data download	no	yes	yes
data connection to Claude or ChatGPT	no	yes	yes
export to a cloud bucket and APIs	no	no	yes
single sign on	no	no	yes

Two of those rows beat us outright and both are worth a buyer's attention. Eleven models against the five surfaces we name is more than twice our coverage, and if your buyers use Copilot, DeepSeek or Meta AI, Evertune reaches them and we do not. Single sign on appears on its top plan and appears nowhere in our own published plan table, which on an enterprise security review is a real difference rather than a presentational one. Its home page names nine surfaces by name, ChatGPT, Claude, Gemini, AI Overview, AI Mode, Perplexity, Copilot, DeepSeek and Meta, which is the longest enumerated list we have read in this category.

The advertising half, and the pricing mechanism underneath it

Evertune is two products sold together. One measures visibility and the other buys advertising, and the second is priced as a share of the media it manages rather than as a flat subscription, with the share falling as the plan rises and a short free trial on the advertising agent. That is a media buying arrangement rather than a software arrangement, and it changes what to settle in the contract: who holds the advertising account, what happens to it when the engagement ends, and how the fee is calculated on managed spend. Those are the terms worth fixing before signature, and a media buying agency, where your money passes through their hands, so buy the transparency terms sets out the clauses that do the work.

If you want measurement without the advertising half, that is the strongest single reason to look at the alternatives, because a point tracker prices the measurement on its own.

What the assistants do with Evertune, measured

Across 1,260 readings from ChatGPT and Gemini in three markets, counting once per reading, Evertune was named six times. All six came from Gemini and none from ChatGPT. It held first place in a numbered shortlist once. In two of the six its name appeared only in the source list at the foot of the answer rather than in the body where the recommendation is made, which is the second highest share of source list only naming in the whole roster.

That gap is the finding worth sitting with. The vendor publishing the most specific measurement claim in the category, and the longest engine list, is named in six readings of 1,260, and a third of those name it as a citation rather than as a recommendation. Publishing more detail than anybody else has not produced more mentions than anybody else. Our own position is the extreme version of the same fact: our name appeared in zero of the 1,260 answers while rivals were named 1,314 times in total.

Which alternative fits which reason for leaving Evertune

• You want the sampling and not the advertising. Ask each shortlisted vendor for its draw count per prompt in writing. Where a vendor publishes prompts, models and frequency, do the multiplication yourself and compare the product, because that number is the comparable one.
• The entry plan is not enough and the next one is a different size of purchase. Compare against vendors whose ladders step in smaller increments, and count prompts rather than plan names. Rankscale publishes five steps and Otterly four.
• You need eleven surfaces. Then Evertune and Rankscale are the two to shortlist on published coverage, and most of the rest of the category, including us, publishes fewer.
• You need single sign on and a cloud export. Both sit on its top plan. Peec publishes single sign on and API access on its top plan too, and Scrunch publishes single sign on with SAML and OIDC on its enterprise tier.

What a buyer asks before switching

Does the entry plan report an averaged figure? The published table puts the hundred samples row on the two upper plans. Ask what the entry plan does instead, and how many readings sit behind a figure on it.

How many prompts does 5,000 credits buy? The pricing page carries a calculator that moves between ten and 1,500 unique prompts, so run your own question count through it before the call rather than after.

Is Evertune recognised by an analyst? Its own newsroom records it as a notable vendor in a Forrester answer engine optimisation technologies landscape for the third quarter of 2026. That is a vendor restating an analyst document, so read the document before quoting the line.

Who did the engines put first instead? Of 397 readings that produced a numbered shortlist, first place went to Semrush 52 times, Otterly 32, Peec 24, Ahrefs 21, Profound 7 and Evertune once. Our own name held it zero times.