Running an A/B test costs nothing at the low end. GrowthBook’s free Starter plan lists “Unlimited feature flags, Unlimited experiments, Unlimited traffic” for up to three users. Statsig’s free Developer plan includes A/B tests and multivariate experiments with 2 million events a month. PostHog bills experiments together with feature flags and charges nothing for the first million requests. Those are live prices on the vendors’ own pricing pages, and none of them is a trial.
What decides whether you can test is traffic, and traffic has not gotten cheaper. On a page converting at 2%, detecting a 20% improvement takes roughly 39,000 visits split across the two versions. If the page you have in mind gets 4,000 visits a month, there is no tool at any price that will hand you an answer this quarter. That is the whole decision, and it happens before anyone looks at a pricing page.
How much traffic does an A/B test need?
Enough that chance stops being the likeliest explanation for the difference you are looking at. On a page converting at 2%, detecting a 20% improvement takes about 19,600 visitors per version, so roughly 39,000 on that page in total. A page already converting at 10% needs about 7,200. Chasing a 10% improvement instead of a 20% one roughly quadruples the requirement.
| Conversion rate today | Improvement you want to detect | Visitors per version | Total on the page |
|---|---|---|---|
| 1% | 20% (1.0% to 1.2%) | 39,600 | 79,200 |
| 2% | 20% (2.0% to 2.4%) | 19,600 | 39,200 |
| 2% | 10% (2.0% to 2.2%) | 78,400 | 156,800 |
| 5% | 20% (5.0% to 6.0%) | 7,600 | 15,200 |
| 10% | 20% (10% to 12%) | 3,600 | 7,200 |
| 10% | 10% (10% to 11%) | 14,400 | 28,800 |
Those figures come from the standard power calculation PostHog publishes in its own experiment docs, at 80% power and 95% confidence, and the 10% row is the worked example printed there. Any sample-size calculator will give you the same numbers.
Two things fall out of that table and both of them get missed. The traffic has to be on the page under test, so a site doing 200,000 visits a month across 400 pages does not clear the gate for the pricing page. And you want the answer inside about four weeks, because past that the season changes, the campaign mix changes, and the team stops caring about the question. Take the total column as the monthly visits that one page needs.
What to do when the page does not have the traffic
Most pages do not clear the gate, and that is fine. Below the line, testing is the slowest way to learn something, so spend the hours here instead, in rough order of value:
- Get the number before the argument. Open analytics, filter to the single page, read last month’s visits and its conversion rate. Two minutes, and it settles whether the rest of this is even a question.
- Watch ten session recordings of that page. Below the traffic gate this is the highest-value hour available to you, and it produces the kind of finding a test cannot (“nobody scrolls past the second block”).
- Read the last month of support tickets and sales objections that mention the page. That is qualitative evidence you already own and have not read.
- Ship the change and read the trend for four weeks. Note the date, name the change in your analytics annotations, and resist opening the graph daily.
- Spend your first real test on whichever page already carries the most traffic, even when a quieter page is the one bothering you.
Write down the question and what you would change if the answer went either way. Teams that skip this step run tests, get results, and do nothing with them, which is a more expensive failure than not testing.
What A/B testing tools cost in August 2026
Here is the range, checked on each vendor’s own pricing page in August 2026. Prices are as published, in the currency each vendor prints.
| Tool | Published entry price | What that covers | Where it runs out |
|---|---|---|---|
| GrowthBook | Free (Starter) | Up to 3 users, 1 project, unlimited experiments, unlimited traffic. Open source and self-hostable. | Visual editor and power calculator sit on Pro at $40 per seat per month |
| Statsig | Free (Developer) | 2M events a month, A/B tests, multivariate experiments, 50,000 session replays | No-code visual web editor starts on Pro at $150 a month, 5M events then $0.05 per 1,000 |
| PostHog | Free tier, then usage | First 1 million feature flag requests a month free, experiments billed with flags | $0.000100 per request in the 1M to 2M band, dropping to $0.000045 above 2M |
| LaunchDarkly | Free (Developer) | 100K experimentation monthly active users at no additional charge, unlimited seats | Only 1K client-side monthly active users free. Foundation is $8.33 per 1k of them per month, billed yearly |
| Convert Experiences | $399 a month (Growth) | 100,000 tested users a month, $299 a month billed annually | Pro is $599 a month. Enterprise is “Price on Request” and annual only |
| Kameleoon | From $495 a month (PBX Starter) | Up to 10 experiments and 50,000 tested visitors a month | Enterprise is “Custom”. Personalization and mobile testing are priced add-ons |
| Amplitude | Free plan, 2M events a month | Limited experiments on Free and Plus | Feature Experiment is “Available for Growth and Enterprise”, both custom-priced |
| VWO | Not published | Growth, Pro and Enterprise tiers listed | No figures on the pricing page, “Schedule a Demo” |
| Optimizely | Not published | Web and Feature Experimentation | “Every Optimizely plan is individually packaged” |
| AB Tasty | Not published | Custom scope | “No fixed plans. Just a custom proposal built around your goals and scope” |
The free plans are real
The four tools at the top of that table are not stripped demos. GrowthBook’s free plan prints unlimited traffic. Statsig’s free plan names A/B tests and multivariate experiments outright. LaunchDarkly’s free plan carries 100,000 experimentation users a month at no additional charge. If your team already pays for one of these for feature flags or product analytics, your experimentation budget for this year is already spent.
There is one consistent catch, and it is the part that matters most to a marketing team. The no-code editor, the thing that lets you change a headline without filing a ticket, is what these vendors charge for.
- GrowthBook puts the visual editor and the power calculator on Pro, at $40 per seat per month. For a three-person marketing team that is $120 a month.
- Statsig puts no-code experiments with a visual web editor on Pro, at $150 a month with 5 million events included.
- PostHog has no-code web experiments in beta through its toolbar, and its docs are honest about the limits. They work for “small edits to text or basic formatting” and warn against single-page applications, because frameworks that re-render “may overwrite any no-code changes you apply.”
- LaunchDarkly is built around feature flags, so the split lives in code. The 1,000 client-side monthly active users on the free plan is the limit that will actually bite on a public website, and at 100,000 the published Foundation rate of $8.33 per thousand works out to about $833 a month, billed yearly.
For a marketing team that has a developer available for a few days once, any of the free tiers is enough. For a team that needs to start and stop tests alone, forever, the honest budget is $120 to $150 a month.
The two mid-tier tools that print a price
Convert publishes Growth at $399 a month for 100,000 tested users, or $299 a month billed annually. Kameleoon publishes PBX Starter from $495 a month, capped at 10 experiments and 50,000 tested visitors. Both are dedicated conversion-testing tools rather than analytics platforms with a testing feature, and both are self-serve enough that you can buy without a call.
Put those caps next to the traffic table. Kameleoon’s 50,000 visitors covers exactly one test on a page converting at 2%, and only if that page is the only thing you test that month. Convert’s 100,000 covers two. Both caps are fair for the money. They are also a good check on how much testing a scale-up marketing team gets through in practice, which tends to be one or two tests a quarter.
The suites that quote by phone
Optimizely, AB Tasty and VWO sell the enterprise end and none of them publishes a figure. Optimizely’s pricing page says “Every Optimizely plan is individually packaged.” AB Tasty’s says “No fixed plans. Just a custom proposal built around your goals and scope.” VWO’s lists Growth, Pro and Enterprise tiers with a demo button and no numbers. Amplitude publishes a free plan and then puts Feature Experiment on Growth and Enterprise, both custom-priced.
We went looking for a defensible number on what these cost in practice and could not get one. The third-party benchmark sites disagree with each other by a factor of five on Optimizely alone, so there is nothing here we would put in front of you as a figure. What the published side does tell you is that price in all of them scales with monthly tested users, which is the same number you just pulled off the traffic table. Walk into the call with it.
These are real products with capabilities the free tiers do not have, including audience targeting, server-side experimentation across a whole stack, and a support relationship with a person attached. If you are running a dozen concurrent tests across five markets, that is what you are buying. Almost nobody reading this is doing that yet.
Why this got cheap
Google Optimize was the free option, and Google shut it and Optimize 360 down on September 30, 2023, pointing users at AB Tasty, Optimizely and VWO. All three of those quote by phone, so the immediate effect was a category that had a free entry point suddenly not having one.
What filled the gap was a different kind of company. Feature-flag and product-analytics tools already had the hard parts built, the traffic split, keeping a returning visitor on the version they saw the first time, the statistics, the event pipeline, and experimentation was a feature they could add to a plan they were already metering. They price by usage and publish the rates, because self-serve signup is how they sell. That is most of the reason a small site can now run a real test for nothing. The rest of it is that wiring a traffic split into a site stopped being weeks of specialist work, and we made the same argument about a different piece of marketing plumbing in the AI control center marketing teams need.
The one part your CMS has to handle
Your CMS needs somewhere sane to keep the second version of the copy, and any serious headless system does this already. Storyblok and DatoCMS are the two we work in most and both handle it comfortably. If the variant text ends up hard-coded in the front end instead, the test still runs, and your editors just lost the ability to change it. That is the failure mode from the invisible time trap of poor editor experience, showing up in a new place.
What to do this week
- Pull the monthly visits and the conversion rate for the single page you most want to test. Sitewide numbers will flatter you.
- Find your row in the traffic table. If the total is higher than that page gets in a month, stop, and work the list above instead. Come back when the traffic does.
- If you clear the gate, check what you already own. PostHog, Statsig, GrowthBook and LaunchDarkly all include experimentation on plans teams already pay for, and finance has probably already approved one of them.
- Only price a dedicated tool if you need the visual editor and nobody in-house will wire a split for you once. That decision runs $120 to $500 a month and you can make it in an afternoon.
- Before any sales call, write down your monthly tested users for that page. It is the number that sets the price in every tool on this list, and going in without it means the quote gets built on someone else’s assumption.
If the wiring is the part that has been sitting undone for two years, that is exactly the work a fractional marketing engineer does. It never reaches the top of a product roadmap, which is how a team ends up paying a licence for a capability it could have had for the price of a few days of work.
Start with the traffic number for one page. It takes ten minutes and it decides everything that comes after it.