AI Trial-to-Paid Conversion: 6% to 8% Is Good, and Retention Decides the Rest

A good free-to-paid conversion rate for an AI product is 6% to 8%, and a great one is 15% to 20%. That is the only published trial to paid conversion rate benchmark that separates AI products from classic SaaS, and it comes from a ChartMogul survey of 200 products run in January 2026. Stripe, which bills many of the fastest-growing AI companies, publishes no conversion benchmark at all. The bigger surprise sits one step later, in retention.

The short answer. For a B2B AI product, ChartMogul's 2026 survey treats 6% to 8% free-to-paid within six months as good and 15% to 20% as great. A trial that requires a card should clear 25%. Set the target against the row that matches your model, then judge it alongside retention, because AI customers who pay once churn far faster than SaaS customers.

The trial to paid conversion rate benchmark table, with methodology

Four publishers come up whenever a founder asks what conversion rate to aim for: ChartMogul, OpenView, Stripe and RevenueCat. They measure different populations with different definitions. Two are surveys, one is billing data from mobile apps, and one publishes no conversion figure at all.

The table records what each publisher states on its own page, opened in October 2026. Where the page does not say something, the table says so. For the wider set of visitor-to-lead benchmarks, including the First Page Sage trial figures, see the comparison of B2B SaaS conversion rate benchmarks, which this post does not repeat.

All rows checked against the publisher's own page in October 2026. "Not stated" means the page does not say.

Two of these rows look comparable and are not. ChartMogul's 8% survey median and its own help centre's 3% median both carry the ChartMogul name. The first is a survey of 200 products across every acquisition model. The second describes ChartMogul's billing customers and publishes no method. Quoting either as "the ChartMogul benchmark" without saying which is a common mistake, and one I would correct in any deck I reviewed.

ChartMogul's 2026 conversion report is the only B2B source with an AI row

The ChartMogul conversion report was written with Kyle Poyar of Growth Unhinged and with ProductLed, and published on 4 February 2026. It surveyed 200 B2B software products by Typeform in January 2026. Respondents were asked to estimate their own figures to the best of their ability.

The sample skews small. 34% of respondents had under $1M in ARR and 29% had $1M to $5M. Only 16% were above $20M. By product type, 55% described themselves as mostly SaaS, 30% as hybrid AI and SaaS, and 9% as AI-native.

The good and great bands by acquisition model

The report frames its results as a good band at the 50th percentile and a great band at the 75th. Across every model combined, the median free-to-paid conversion rate was 8%. That 8% is often quoted as a free trial median. It is not one. It covers freemium, trials and every other entry point together.

By model, standard freemium sits at 3% to 5% good and 8% to 12% great. Free trials without a card sit at 4% to 6% good and 10% to 15% great. Trials that require a card sit at 25% to 35% good and 50% to 60% great. The report states that card-required trials convert at about 30%, more than 5 times trials that do not require one.

The AI row, and how small it is

The report gives AI-native and hybrid AI products a combined good band of 6% to 8% and a great band of 15% to 20%. That is slightly above the no-card trial band and level with B2B products overall. It is the figure this post leads with, because nothing else on the public record does the same job.

It deserves a heavy discount. The row merges two groups that behave differently. AI-native respondents were 9% of 200, which by my arithmetic is about 18 products. The hybrid group, around 60 products, therefore dominates the combined figure. In my view the row tells you more about SaaS companies that added AI than about products built on a model from day one.

The report does split one thing by type: the entry point. Free trials are the primary entry point for 61% of SaaS respondents, 51% of hybrids and 43% of AI-native products. Why AI-native teams ration free access is set out in the piece on how compute put a meter on the free tier.

The report also models outcomes per 1,000 visitors. Freemium yields 90 signups and 5 paying customers. A no-card trial yields 45 signups and 3.6 payers. A card-required trial yields 35 signups and 10.5 payers. The card trial wins per visitor here, unlike some agency data, because the survey's card trials lose fewer signups at the top.

OpenView's 2023 freemium conversion benchmark is still the baseline everyone quotes

Before ChartMogul's report, the most quoted freemium conversion benchmark came from OpenView's product benchmarks programme. Its 2023 edition was run with Pendo and Growth Unhinged and written up by Kyle Poyar and Lenny Rachitsky on 1 August 2023. It drew on data from 1,000+ products, focused on B2B SaaS.

OpenView itself stopped investing in new companies in 2024, and its 2023 benchmarks page now returns a 404. The numbers survive mostly through the Lenny's Newsletter write-up. That is a fragile citation path for a figure that sits in many board decks.

The six-month definition both surveys share

The 2023 post defines free-to-paid conversion as new accounts that begin paying within their first 6 months, divided by all new accounts created. It measures accounts, not users. ChartMogul's 2026 report uses the same six-month window, which makes the two surveys the most comparable pair in this post.

On that definition, 3% to 5% is good for a self-serve freemium product and 6% to 8% is great. With a sales-assist motion, freemium rises to 5% to 7% good and 10% to 15% great. Free trials sit at 8% to 12% good and 15% to 25% great, with no split by card requirement.

Set side by side, the freemium bands barely moved. Good was 3% to 5% in 2023 and is 3% to 5% in 2026. The free trial band looks lower in 2026, at 4% to 6% good without a card, but that is mostly because the 2026 report splits card and no-card trials while 2023 did not.

The 2023 data also found that developer-focused companies had a median conversion rate of 5%. That matters for AI coding tools, which sell to the population that historically converts at the low end. I would not set a coding-agent target above the developer median without first-party evidence that your buyers behave differently.

Stripe publishes AI growth and trial abuse figures, not a conversion benchmark

Stripe processes payments for a large share of AI companies, so founders often assume it publishes a trial-to-paid benchmark. We found none on its blog, newsroom or report pages. What Stripe does publish is useful, but it answers different questions.

Growth speed for the top 100 AI companies

A June 2025 Stripe analysis found that the top 100 AI companies on Stripe reached $1M in annualised revenue in a median of 11.5 months. Stripe describes that as about 4 months ahead of the fastest-growing SaaS companies at the height of the subscription boom.

That is a survivorship figure about winners. It tells you how fast the top 100 grew, not how often their trials convert. Using it to set a conversion target for a product outside that group is a category error.

Trial abuse inflates your denominator

The more relevant Stripe number is about fraud. Its Sessions 2026 release, dated 29 April 2026, says that across AI services on Stripe, 1 in 6 attempted sign-ups is made by a bad actor and free trial abuse has more than doubled in 6 months. It adds that Radar blocked more than 3.3 million risky sign-ups for 8 high-growth AI businesses in a single month.

No sample, definition or baseline sits behind either claim, so treat them as directional. The direction still matters for any conversion target. If abusive sign-ups stay in your trial count, your conversion rate falls without any change in genuine demand. If you filter them out, your rate rises. Two AI companies with identical customers can report very different trial-to-paid rates purely because of how they clean the denominator.

RevenueCat has the AI conversion data B2B lacks, but it is mobile and consumer

RevenueCat sits between app stores and subscription apps, so it sees billing events directly rather than survey answers. Its State of Subscription Apps 2026 covers more than 115,000 apps and more than $16B in revenue, with 2025 as the target window. It defines trial-to-paid conversion as the share of free trial starts that convert into a paid subscription.

Trial length moves conversion, and AI apps hit a ceiling

The 2026 report states that trials of 17+ days convert 70% better than short trials, at 42.5% against 25.5%. A September 2026 RevenueCat analysis of 17,000+ apps, covering trial starts from August 2025 to July 2026, then split the result for AI apps.

For monthly plans, AI apps converted at 38.2% on 5 to 9 day trials and 38.5% on 10 to 16 day trials. On 17 to 32 day trials, conversion fell to 31.8%. RevenueCat's chart notes that AI apps have lower trial conversion and first renewal rates than non-AI apps at every trial length. Its own explanation for preferring shorter AI trials is cost: every free session carries a real, variable compute charge.

The same 2026 report finds that AI-powered apps generate 41% more revenue per payer than other apps, but churn 30% faster. That is the consumer version of the B2B pattern below. A higher price gets accepted at the paywall, then fewer customers stay to pay it again.

The caution is scale and context. These are phone apps sold to consumers through app stores, often on weekly or annual plans. A 40% trial conversion on a meditation or photo app tells you almost nothing about a B2B AI agent sold at $200 a month. I would use RevenueCat for the direction of AI effects, never for the level.

Where AI product monetization differs from classic SaaS conversion

Put the four sources together and one pattern holds. AI products convert at roughly SaaS rates. They differ on what happens before the trial and after the first payment.

Retention is where the AI benchmark breaks

ChartMogul's December 2025 retention study looked at about 3,500 software companies, including roughly 200 AI-native ones, filtered to at least $250k ARR. B2B SaaS had a median net revenue retention of 82%. B2C SaaS had 49%. AI-native companies had a median gross revenue retention of 40% and net retention of 48%, worse than consumer SaaS on gross retention.

Price point explains most of it. AI-native products priced above $250 a month showed 70% gross and 85% net retention, essentially B2B SaaS. Products at $50 to $249 showed 45% and 61%. Products under $50 showed 23% and 32%. The study does not publish per-bucket sample sizes and warns that some buckets fall to around 50 companies.

A September 2026 ChartMogul study of 10.5 million purchases across 4,029 private software companies found that month-one upgrade rates were nearly identical for SaaS and AI-native products. After that the curves diverged, and SaaS ended with a nearly 3 times higher overall upgrade rate over 12 months. The author suspects AI-native products push harder on usage and credit top-ups instead, which would not count as a plan upgrade. Why credit-based billing confuses buyers is covered in the analysis of AI billing in credits and tokens.

The practical consequence is simple. A SaaS conversion target assumes the converted customer stays and grows. For an AI product, that assumption is the weakest part of the plan, and a conversion rate measured over six months says nothing about it. The same gap shows up at consumer scale in ChatGPT's paid conversion rate, stuck near 5%.

Where this benchmark comparison is weakest

This post leans on one AI-specific figure, and the case against relying on it is strong. Three weaknesses deserve stating plainly.

Two surveys, both self-reported, both small where it matters

Both ChartMogul's 2026 report and the 2023 OpenView data are surveys. Respondents estimated their own conversion rates, and companies with good numbers are more likely to answer benchmark surveys. Neither publishes a margin of error. The AI-native subgroup is about 18 products, merged with a larger hybrid group. A row built that way can move several points with a handful of different respondents.

The strongest counter-case: conversion targets may not matter for AI at all

One reasonable view says a trial-to-paid target is the wrong goal for most AI products. If gross retention under $50 a month is 23%, a team that doubles conversion simply doubles the number of customers who leave. On that reading, price point and retention are the decisions, and conversion is a downstream number. I find that argument mostly right for consumer-priced AI tools and mostly wrong above $250 a month, where retention already matches SaaS.

We also left out First Page Sage's trial figures, which another post on this site covers, and the gated Stripe report "Indexing the AI economy", which sits behind a form we did not complete. Either could contain figures that change parts of this comparison.

How to set a trial to paid conversion target for an AI product

My recommendation is to set a conversion target and a retention floor together, and never one without the other. Pick the benchmark row that matches your entry point and price, then adjust it for what you know about your buyers.

Illustrative only. Zan Digital arithmetic multiplying a fixed 7% conversion rate by ChartMogul's December 2025 net retention medians. Net retention is a revenue measure, so "paying-equivalent" means revenue held relative to the converted cohort, not a count of logos.

The table holds conversion constant to isolate retention. At the same 7% rate, a cheap AI product keeps about a third of the revenue an expensive one keeps. A team that lifts conversion from 7% to 10% under $50 a month gains less than a team that moves its price into the next bucket. The margin side of that decision is covered in the AI unit economics margin trap.

Two adjustments come next. Remove abusive and duplicate sign-ups from the trial count before you measure, and write down how you did it. Then measure conversion on the six-month window both surveys use, so your number is comparable to theirs. If agents rather than people start your trials, the denominator changes again, a problem set out in the piece on product-led growth when the user is an agent.

Frequently asked questions

Where to start this week

Pull your trial cohort from six months ago and compute one number: accounts that paid within six months, divided by accounts that started, after removing sign-ups your fraud tool flagged. Write the definition beside the result. You now have a figure you can honestly place against ChartMogul's 6% to 8% row.

Then put a second number next to it: gross revenue retention for customers who converted from trials a year ago. If that is near 70%, chase conversion. If it is nearer 23%, the next experiment is your price point or plan length, not your onboarding email.

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