Evertune Review 2026: The 100-Run Sampling Claim

Evertune is a New York AI visibility platform that measures how brands appear across AI models by sampling each prompt up to 100 times per model. That sampling claim is the reason this review exists, because it is the one methodological question we think decides whether a number in this category means anything, and Evertune is the only vendor we have found that puts it on the homepage.

We are going to say something unusual for a competitor review: on that specific point, they are right.

Disclosure: EchoWi competes with Evertune. Its model list, sampling claim and audience were read from its own site on 5 August 2026.

The short version

1. Up to 100 samples per prompt per model, against the single check most tools report. This is the difference between a measurement and a coincidence.
2. 11 models benchmarked, including DeepSeek, which almost nobody else in this comparison covers.
3. Aimed at advertisers and agencies, not at founders. It claims over 700 of them, naming Roku, Athenahealth and WPP.
4. $19 million raised across a $4M seed and a $15M Series A in August 2025, from Felicis Ventures and Eniac Ventures.
5. No public pricing. Demo request only, which is consistent with the buyer they are built for.

Why the sampling number is the whole review

AI assistants re-plan retrieval on every request. Ask the same question twice and you can get two different brand lists, which means a tool reporting one measurement per prompt is reporting a draw, not a rate.

We measured this ourselves. Asking Google’s AI Overview in Spain, in Spanish, which tool is best for measuring brand visibility in ChatGPT, three runs returned 11 distinct cited domains. Five appeared in all three runs. Four appeared once. A single check would have given a one-off the same weight as a domain cited every time.

That is the failure mode Evertune’s headline is aimed at, and getting to 100 samples per prompt per model is expensive: every run is a fresh upstream call, and they cannot be parallelised without correlating the samples you are trying to vary. Charging enterprise prices for it is a coherent position rather than a markup.

Where we would push back: a high sample count makes a number stable, not causal. Knowing your mention rate is 34% with tight error bars still does not tell you that a change you made moved it. That needs a control page and a before and after, which is a different discipline from sampling, and no vendor here has published a controlled result. Sampling solves precision. It does not solve attribution.

How many runs you actually need

Nobody in this category publishes this, so here it is. A mention rate is a proportion, and the uncertainty around a proportion has a formula that has been settled for a century.

For a mention rate of about 30%, this is what each sample size buys you, as a 95% confidence interval:

Read the hundred-run row carefully, because it is the interesting one. Evertune’s headline sampling, the best disclosed in this market by a wide margin, still only tells you your mention rate is somewhere between 21% and 39%.

That is an enormous improvement on one run, which tells you nothing at all. It is also not the precision instrument the category’s dashboards imply, and we would rather say that in a review that otherwise praises them.

Now the harder number. Suppose you want to know whether a change you made moved your mention rate from 30% to 35%. Detecting a five point difference reliably, at conventional 95% confidence and 80% power, takes roughly 1,400 runs in each condition. Not 100. Not per month. Per condition, for one prompt.

What follows from that is the honest operating advice in this whole category:

• Stop chasing small movements. A three point change in a dashboard built on a hundred runs is noise wearing a number’s clothes. Most of the week-on-week movement teams report to their boards is not real.
• Aggregate across prompts. Fifty prompts at a hundred runs is five thousand observations of your brand. The per-prompt rate stays noisy while the portfolio rate gets usable, which is why the sensible unit of measurement is the prompt set and not the prompt.
• Widen the window. You cannot afford 1,400 runs a day. You can afford a hundred a day for two weeks, and comparing a fortnight against a fortnight gets you there.
• Use a control. A comparable page you did not touch absorbs the platform’s own drift, which is the largest source of movement nobody is measuring.

This does not undermine Evertune’s claim, it explains it. Up to 100 runs per prompt per model is the highest disclosed sampling in the category and it costs real money to produce. Our point is narrower: even that number has limits, and any vendor, us included, whose interface implies otherwise is flattering the data.

The arithmetic above uses the standard normal approximation for a binomial proportion at p = 0.30, and the power calculation assumes a two-sided test at α = 0.05 with 80% power. Both are textbook and both are checkable, which is more than can be said for most numbers in this market.

What it covers, verified

Two things stand out in that list.

DeepSeek coverage is rare. Of the tools in our comparison, almost none track a Chinese-developed model. Evertune does, though it stops short of Doubao and Qwen.

EverPanel is the more interesting asset. Most tools ask you to write your own prompt set, which means your measurement is only as good as your guess about how buyers phrase things. Grounding prompt selection in observed behaviour attacks a real weakness, and it is not something a competitor can copy quickly.

Where Evertune is the right call

• You publish the numbers externally. If a mention rate goes in a board deck or a client report, sample size is the first question a sceptic asks, and this is the strongest answer available.
• You are an agency with many clients. The audience, the panel and the benchmarking are built for that shape.
• DeepSeek matters to your market. Few Western tools cover it at all.
• You already buy media. The positioning spans paid and organic, which fits a team that does both.

Where it is the wrong call

• You are small and want to start today. No published price and a demo-first motion means weeks, not an afternoon. Promptmonitor is $29 a month with the price on the page.
• You need attribution, not precision. A tighter number is not a causal one. Measuring whether a specific change worked, against a control, is a different job.
• Your market is China. DeepSeek is covered, Doubao and Qwen are not.
• You want to compare vendors on price. You cannot include Evertune in that table, the same problem Peec AI has.

What we could not verify

• The price. Not published anywhere on the site.
• Whether 100 samples is the default or the ceiling. “Up to 100x” is a maximum. The number that matters is what a standard plan actually runs, and that is worth asking in the demo.
• How the 11 models map to the 9 named. The site benchmarks across 11 and names 9. Ask which two are missing from the list.
• Accuracy against other tools. Nobody has published a controlled side-by-side test of these platforms, us included.

Evertune versus the alternatives

Prices verified between 6 and 8 August 2026 from each vendor’s own page. The full comparison covers every tool we could verify, including one that shut down in October 2025.

Common Questions About Evertune

How much does Evertune cost?

Evertune does not publish pricing. The site routes to a demo request. Given the audience it names, over 700 advertisers and agencies including WPP, the pricing is very unlikely to sit near the $29 to $49 self-service tier.

What AI models does Evertune track?

It names ChatGPT, Claude, Perplexity, Gemini, Gemini AI Mode, Google AI Overviews, Copilot, DeepSeek and Meta AI, and describes benchmarking across 11 models. It does not cover Doubao or Qwen.

How much funding has Evertune raised?

$19 million across two rounds: a $4 million seed and a $15 million Series A announced in August 2025, backed by Felicis Ventures and Eniac Ventures. It was founded in 2024 and is based in New York.

Does sampling a prompt 100 times actually matter?

Yes, and it is the most underrated specification in this category. AI assistants re-plan retrieval per request, so identical questions return different brand lists. In our own three-run test on Google AI Overview, 4 of 11 cited domains appeared only once. A single check cannot tell a stable citation from a coincidence.

Is a higher sample count the same as proving something works?

No, and the distinction matters commercially. Sampling gives you a precise number. It does not tell you that your work caused a change, which needs a control page and a before-and-after comparison. Precision and attribution are different problems.

What is the best Evertune alternative?

For published pricing and self-service, Promptmonitor at $29 a month. For a European counterparty, Peec AI in Berlin. For measuring whether a change worked across 12 engines including Doubao and Qwen, that is what we build EchoWi for.

Where this leaves you

Evertune has picked the right thing to be loud about. In a category where most vendors compete on dashboard screenshots, leading with sample size is a methodological argument, and it is the correct one.

What it buys you is a number you can defend. What it does not buy you is proof that anything you did caused the number to move, and no tool on the market sells that yet, including ours. Ask every vendor in this category how many runs sit behind a reported figure. Evertune is the only one that answers before you ask.

Next: the Peec AI review, the Scrunch review and the Sitecore acquisition, and the full comparison with verified prices.