Free-to-paid conversion rate benchmarks for freemium, opt-in trials and card-required trials

The free-to-paid conversion rate benchmark for SaaS is a blended median of about 8 to 9 percent, with a wide spread by entry model. A January 2026 study of 200 software products put the median at 8%, while trials that require a credit card see about 30%. Freemium counts as good at 3% to 5% and no-card trials at 4% to 6%, so compare your rate only to your own model.

What is a good free-to-paid conversion rate?

The short answer: a blended median of about 8 to 9 percent, with a wide spread around it and a much higher figure for trials that ask for a card upfront.

The study behind the 8 percent figure was conducted in January 2026 and included 200 software products. It defined conversion as the percentage of leads or free signups that convert to become a paying customer within six months. A typical respondent was between $1 to $10 million ARR, meaning annual recurring revenue.

Across all 200 products, the median free-to-paid conversion rate across all products was 8%. But very few products actually have an 8% conversion rate. Trials that ask for a card behave very differently: free trials that require a credit card see 30% free-to-paid conversion.

An older survey of 600+ SaaS businesses on product-led growth asked about investment plans for 2022 and found that overall, 9% of free accounts convert to paid accounts.

Why one number misleads

The spread is wide. The January 2026 study found a 10x conversion difference between the top 20% of self-serve products and the bottom 20%. Among its free trial products, one-in-five (20%) products see a free-to-paid conversion rate below 2.5%, while roughly one-in-four (23%) see a conversion rate higher than 25%.

In our reading, the median sits in a gap between clusters, so comparing your rate to 8 percent tells you little until you compare it to products with your own entry model.

The same study published bands by model:

Entry model	Good	Great
Freemium, self-serve	3% to 5%	8% to 12%
Free trial, no card	4% to 6%	10% to 15%
Free trial, card required	25% to 35%	50% to 60%

Sources for the bands: freemium, free trial, card required.

A high rate on a card-required trial is not automatically a better outcome. The report notes that introducing a credit card requirement could blunt signups, hurting the overall number of paying customers.

A worked example

Take 1,000 signups as a round example input, not a benchmark. At an 8 percent rate, 1,000 signups give 80 paying customers. A card-required trial converting at 30 percent needs only 267 signups to reach 80 customers, since 80 divided by 0.30 is about 267. So the useful test is whether a card wall cuts your signups by more or less than that, and you can only answer it with your own data.

This week, do three things:

1. Measure your rate over the same six-month window the study used.
2. Compare it only to the band for your entry model.
3. Track signups and paid customers together, so a rise in the rate does not hide a drop in volume.

What is a typical freemium conversion rate?

Freemium has no typical rate. It has two clusters with a thin middle. In the 2026 conversion report summarized by Userpilot, the distribution under the median was "almost bimodal": about 25% of freemium products converted below 2.5%, another 29% landed between 2.5% and 7.5%, and roughly 25% achieved 10-15%.

Freemium-to-paid rates in this agency client data ran from 2.1% in EdTech to 5.6% in LegalTech.

Freemium-to-paid conversion by industryPer cent

1. EdTech2.1%
2. Healthcare/MedTech3.3%
3. RegTech4.7%
4. Fintech4.8%
5. ERP5.1%
6. LegalTech5.6%

Freemium-to-paid conversion by industry

Item	Value, %
EdTech	2.1%
Healthcare/MedTech	3.3%
RegTech	4.7%
Fintech	4.8%
ERP	5.1%
LegalTech	5.6%

So a product at 3% and a product at 12% can both be ordinary freemium businesses. In our reading, the useful question is which cluster your product sits in and why, not how far you are from the middle.

What agency client data shows by industry

First Page Sage analyzed conversion data from 80+ SaaS clients with freemium models between 2022 and 2026. In our reading, its freemium-to-paid figures sit lower and tighter than the survey's spread.

Industry	Freemium to paid
Education technology (EdTech)	2.1%
Healthcare technology (MedTech)	3.3%
Regulatory technology (RegTech)	4.7%
Financial technology (Fintech)	4.8%
Enterprise resource planning (ERP)	5.1%
Legal technology (LegalTech)	5.6%

As an example, not a benchmark: a cohort of 1,000 free signups converting at the EdTech rate of 2.1% gives 21 paying accounts, and the same 1,000 at the LegalTech rate of 5.6% gives 56.

Why the six-month window moves the number

The study behind the bimodal spread counted conversion within six months, as covered above. Freemium users have no deadline, so many upgrade late. A shorter window cuts them off and a longer one keeps adding them.

First Page Sage puts it directly: "Without a defined window, typically 90 or 180 days, your freemium-to-paid rate will drift upward over time and become impossible to benchmark accurately." We think that is the main reason two freemium figures from different sources rarely line up.

Do this before you compare yourself to any figure here:

1. Group free signups into a cohort by signup month or quarter.
2. Fix the window at 180 days so you match the six-month studies.
3. Drop dormant accounts from years past out of the denominator, since they suppress the rate.
4. Report the rate per cohort, then check whether newer cohorts beat older ones at the same day count.

If your 180-day cohorts sit under 2.5%, you are in the lower cluster. Fix activation and time to value first, before pricing.

Do card-required trials convert better?

On trial-to-paid rate alone, yes. The card-required rate from the 2026 study cited above is more than 5x the rate of trials that don't require one. Few teams take that route: among free trial products, 20% require a credit card upfront.

Agency data shows the same pattern. First Page Sage pooled data from 86 SaaS companies, and in it organic opt-out trials, where a card is required, converted 48.8% to paid. Organic opt-in trials, with no card, converted 18.2%. The agency's explanation is that with a card on file, "their decision to pay the subscription fee is often passive" (First Page Sage).

The denominator does the work

A card requirement shrinks the trial pool before anyone sees the product. Organic visitor-to-trial conversion was 8.5% for opt-in trials and 2.5% for opt-out trials.

In our reading, the high trial-to-paid rate mostly reflects who is left in the denominator: people committed enough to enter a card, plus some who forget to cancel. It does not show that the product found more buyers.

Here is a worked example with 10,000 organic visitors. It is an example input, not a benchmark.

Stage	Opt-in, no card	Opt-out, card required
Visitor to trial (organic)	8.5%	2.5%
Trial to paid (organic)	18.2%	48.8%
Paying customers from 10,000 visitors (worked example)	154.7	122

On the opt-in path, 10,000 visitors at 8.5% gives 850 trials, and 850 trials at 18.2% gives 154.7 customers. On the opt-out path, 10,000 visitors at 2.5% gives 250 trials, and 250 trials at 48.8% gives 122 customers. The no-card path ends with 154.7 customers against 122, a gap of 32.7, even though its trial-to-paid rate is lower.

One more check before you copy the higher figure into a plan. In that dataset, a conversion is defined as even a single paid month. A card-required trial can look strong in month one and lose those customers in month two.

What to do this week

1. Report visitor-to-paid, not only trial-to-paid. That is the number that compares the two models fairly.
2. Split results by signup cohort and track month-two and month-three retention for card-required converts against your monthly churn benchmarks.
3. If you test adding or removing a card requirement, size the test on visitor-to-paid with the A/B test sample size calculator, because that rate is small and needs more traffic to read.
4. Before adding friction, check time to value. We think a card requirement suits products that show their value inside the trial window, and hurts those that need longer to prove it.

Worked example: net paying customers from 10,000 visitors under each model

Rates only mean something once you multiply them through the whole funnel. Here is the same traffic run through each model. The 10,000 visitors are an example input, not a benchmark.

For the trials we use First Page Sage's 2026 figures. Opt-in trials convert 7.6% of website visitors into trial users, and 17.3% of those trials go on to pay. Opt-out trials turn 2.7% of visitors into trials and 49.1% of those trials into payers. For freemium we use the firm's organic traffic figures: 13.3% of visitors sign up and 2.6% of free users convert.

Model	Visitor to signup	Signup to paid	Signups per 10,000 visitors (our worked example)	Payers per 10,000 visitors (our worked example)
Opt-in trial	7.6%	17.3%	760	about 131
Opt-out trial	2.7%	49.1%	270	about 133
Freemium	13.3%	2.6%	1,330	about 35

The arithmetic, line by line:

• Opt-in: 10,000 visitors at 7.6% gives 760 trials, and 760 trials at 17.3% gives about 131 payers.
• Opt-out: 10,000 visitors at 2.7% gives 270 trials, and 270 trials at 49.1% gives about 133 payers.
• Freemium: 10,000 visitors at 13.3% gives 1,330 free users, and 1,330 free users at 2.6% gives about 35 payers.

Two things stand out. The card wall's high paid rate is almost exactly cancelled by its low signup rate, so opt-in and opt-out land within a couple of payers of each other. And remember the opt-out count includes anyone who paid for a single month, so check it against monthly churn before you call it a win.

Freemium looks far behind here. The 2026 Growth Unhinged survey found something different: free trials convert slightly higher than freemium, but "this difference gets wiped out upon accounting for signup rates." In our reading, both can be true. Averages hide the spread covered earlier, and a freemium product in the upper band closes the gap. To match the opt-in trial, freemium needs 131 payers out of 1,330 free signups, a paid rate just under 10%.

Run it on your own funnel this week

1. Pull visitors, signups and payers for one quarter's cohort, using a fixed conversion window.
2. Fill in the table with your rates, not the averages.
3. Multiply payers by revenue per customer and set it against acquisition cost to get your payback period.
4. If two models land close, test the cheaper switch first, such as adding a reverse trial to a free plan.

How do I calculate trial conversion rate?

There are two formulas, one for each model. Pick the one that matches how users enter, and write down your definition of a conversion before you count anything.

Trial conversion rate. Baremetrics gives it as (Converted Trial Users ÷ Total Trial Users) × 100. The denominator is the total number of trial accounts started during that same period, not everyone who happens to be on a trial today. Baremetrics also describes a rolling version: (Number of converted trials in the past 30 days / Number of new trials in the past 30 days) × 100.

Free-to-paid conversion rate. For freemium, the definition used in the Lenny's Newsletter benchmark is new accounts who begin paying within their first 6 months, divided by total new accounts created during the measurement window. The fixed window is what makes it comparable.

Why a monthly rate on the whole free base misleads

The common shortcut divides this month's upgrades by everyone on the free plan. Kissmetrics shows the arithmetic: 10,000 free users at the start of the month, 150 of them upgrade during it, so the free-to-paid conversion rate is 150 / 10,000 = 1.5%.

That number mixes users who signed up last week with users from two years ago. Kissmetrics notes that two products with identical 3% monthly rates can have completely different economics depending on how fast each converts.

A cohort sheet you can copy

Make one row per signup month per model. The denominator never changes once the month closes. The paid columns fill in as each cohort ages.

Signup month	Model	Denominator (new accounts that month)	Cumulative paid by day 30	Cumulative paid by day 90	Cumulative paid by day 180
[YYYY-MM]	Freemium	New free accounts created	Count, then count ÷ denominator	Count, then count ÷ denominator	Count, then count ÷ denominator
[YYYY-MM]	Opt-in trial	New trials started	Count, then count ÷ denominator	Count, then count ÷ denominator	Count, then count ÷ denominator
[YYYY-MM]	Card-required trial	New trials started	Count, then count ÷ denominator	Count, then count ÷ denominator	Count, then count ÷ denominator

Three rules keep the sheet honest:

• Fix what counts as paid. Baremetrics points out that some platforms only count a conversion after the first invoice is paid, even if the customer cancels shortly after. Choose one rule and note it in the sheet header.
• Leave young cohorts open. Conversion rates for recent cohorts may shift as more users from those groups convert over time, so mark any cell whose window has not closed yet.
• Compare across rows, not within one. Read down the day 90 column to see whether newer cohorts convert faster. Then compare the day 180 column with the benchmark ranges in the earlier sections.

Pair the sheet with your activation numbers. In our reading, a cohort that converts slowly often has a time-to-value problem rather than a pricing one.

How long should a free trial be?

Fourteen days is the default most teams pick. In ChartMogul's 2026 SaaS Conversion Report, the most common trial length was 14 days, at 62% of products, followed by 7 days and 30 days at 14% each. That tells you what is common. It does not tell you what fits your product.

Fourteen-day trials were used by 62% of products, with 7-day and 30-day trials at 14% each.

Most common free trial lengths% of products

1. 14 days62 % of products
2. 7 days14 % of products
3. 30 days14 % of products

Most common free trial lengths

Item	Value, % of products
14 days	62 % of products
7 days	14 % of products
30 days	14 % of products

Set the length from time to value instead. In the new user journey Growth Unhinged described, the activate stage is where users "actually realize the value that they were promised," and the goal there is to "reduce time-to-value and guide users to their ‘aha’ moment in the product."

This ties to a core product-led growth pillar, one of five listed in Amplitude's write-up: "Show value before asking users for money". A trial that ends before most users reach activation asks for money first. We think that is where trial length quietly costs conversions.

In our reading, activation is also rarer than teams assume. OpenView's 2022 Product Benchmarks report found only 20 to 30% of new users reach activation at standout product-led companies. So size the window around the users who do activate, not around everyone.

A method to set your trial length

1. Define one activation event. Pick the action that shows a user got the value they signed up for, such as a first report shared or a first integration live.
2. Pull a recent cohort of trial starts and record, for each user who activated, the number of days from signup to that event.
3. Sort those users by days to activation and find the day by which the large majority had activated.
4. Add a few days of buffer for the decision to pay, then round to a length you can explain on the pricing page.
5. If your current trial ends well before that day, test a longer window. If almost everyone activates in the first few days, test a shorter one.

Worked example (an example, not a benchmark): say 10,000 people started a trial last quarter and 1,000 of them reached the activation event, an activation share of 10%. Sort those 1,000 by days to activation. If most of them activated after day 14, a 14-day trial is cutting off users mid-path, and a longer window is worth testing. Check our activation rate benchmarks to judge whether the 10% itself needs work first.

One more input. The same report found 80% of free trial products have human touchpoints when an enterprise user enters the trial. In our reading, sales-assisted trials can run on their own clock, so measure their time to activation separately from self-serve users.

Should I switch from freemium to a trial? Steps to take this week

Do not switch models because of a benchmark. Switch because your own numbers, split the right way, point there, and only after a test on one slice of traffic.

1. Segment your conversion three ways

Pull the last two quarters of signups and break free-to-paid conversion by model, annual contract value (ACV) band and signup source (organic, paid, referral). A blended rate hides the segment that is carrying you. In ProductLed's survey, which asked 600+ SaaS businesses, products with an ACV of $1K to $5K had a 10% median conversion to paid, above the other bands. Compare your own bands against your own history first.

2. Score free accounts as product-qualified leads

A product-qualified lead (PQL) is a free account whose usage signals it is ready to buy. ProductLed found that when PQLs are used, the conversion rate for free accounts is 3x higher, yet only 25% of companies report using them. Pick two or three signals tied to activation, flag accounts that hit them, and route those to an upgrade prompt or a sales touch. Do this before you change models. It may close the gap on its own.

3. Keep freemium where the volume math works

Userpilot, citing Kyle Poyar's 2026 report, put it at 90 signups for freemium against 45 for free trials per 1,000 visitors, a gap of 45 accounts. Userpilot adds that this math only works at scale, and calls freemium rarely recommended for B2B SaaS products (business-to-business software). If your free tier spreads through sharing and a growth loop, keep it.

4. Test a reverse trial or a card requirement on one segment

A reverse trial gives new users temporary access to all premium features and then drops them to the free plan. Userpilot suggests running it long enough for users to actually adopt a premium feature, and cites ChartMogul data where reverse trials convert at a “good” rate of 4-6%. Run it, or the card-required trial covered earlier, on one source, such as paid search. Size the test with the sample size calculator and log it in the experiment backlog.

5. Judge on net payers, not rate

A trial arm can post a higher rate and still produce fewer customers. Use this scorecard per cohort, for example per 1,000 visitors:

Metric	Control arm	Test arm
Signups	fill in	fill in
Paid by day 60	fill in	fill in
Cancelled in first paid month	fill in	fill in
Net payers (paid minus cancelled)	fill in	fill in

Our decision rule: switch only if the test arm wins on net payers and holds that lead into month two of retention.

What this page does not cover

This page does not cover how to set price levels, conversion in sales-led enterprise deals or churn benchmarks in detail. It describes conversion rates and methods for measuring them, and it is not financial or legal advice.

Related on Race to Scale: What is a good activation rate? Benchmarks by product type and how to define yours; SaaS churn rate benchmarks by price point and segment; A/B test sample size and duration calculator, with the formula.