Expert Guide

The Best A/B Testing Tools in 2026

Platforms for finding out whether the change actually worked, instead of assuming it did.

Louis CorneloupFounder, Dupple · 930,000+ readers · Updated Sep 2026

Product links may be affiliate links. How we rate 5 tools compared

TL;DR

The best A/B testing tools in 2026 are Wingify (formerly VWO, now merged with AB Tasty) for marketers who want a visual editor plus analytics in one platform, Optimizely for enterprise experimentation at scale, PostHog for product teams who want testing bundled with analytics and feature flags, GrowthBook for open-source warehouse-native experimentation, and Convert Experiences for teams that want a visual editor with published prices. Most teams overestimate how much traffic they have, which matters more than the tool.

Key facts5 tools compared: Wingify (formerly VWO), Optimizely, PostHog, GrowthBook, Convert Experiences

Updated: September 25, 2026
Top pick: Wingify (formerly VWO) (best for: Marketing teams wanting testing plus behaviour analytics in one platform)
Top pick price as of September 25, 2026: Wingify (formerly VWO): Custom quote (Growth, Pro, Enterprise), based on monthly active users; 30-day free trial

5 tools compared: Wingify (formerly VWO), Optimizely, PostHog, GrowthBook, Convert Experiences
Optimizely (best for: Large organisations running many experiments across teams): Custom quote (Enterprise); part of a digital experience platform
PostHog (best for: Product teams that want experiments, feature flags and analytics together): Free tier, then usage-based paid plans; open source and self-hostable
GrowthBook (best for: Data teams that want experiments on their own data warehouse): Free to self-host and free cloud tier; paid plans above that

A/B testing tools are easy to buy and hard to use well, and the reason is arithmetic rather than software. Detecting a realistic 5 percent lift on a 3 percent conversion rate takes tens of thousands of visitors per variant. Teams without that traffic run tests that end inconclusively, call the winner anyway, and slowly build a strategy on noise.

Pick a tool for how you will build tests, but be honest about whether you can run them.

Top Picks

Based on features, real-world fit, and value for money.

Best A/B Testing Tools in 2026: 5 tools compared, updated Sep 2026
Tool	Pricing	Best for
Wingify (formerly VWO)	Custom quote (Growth, Pro, Enterprise), based on monthly active users; 30-day free trial	Marketing teams wanting testing plus behaviour analytics in one platform
Optimizely	Custom quote (Enterprise); part of a digital experience platform	Large organisations running many experiments across teams
PostHog	Free tier, then usage-based paid plans; open source and self-hostable	Product teams that want experiments, feature flags and analytics together
GrowthBook	Free to self-host and free cloud tier; paid plans above that	Data teams that want experiments on their own data warehouse
Convert Experiences	From $399/mo (Growth, 100K tested users) or $299/mo billed annually; 15-day free trial, no card	Mid-sized teams that want a visual editor and published prices without an enterprise contract

Pricing read from each vendor's own published pricing page, checked Sep 2026. Every vendor here publishes a price.

Wingify (formerly VWO)

Top Pick

Best for: Marketing teams wanting testing plus behaviour analytics in one platform

PricingCustom quote (Growth, Pro, Enterprise), based on monthly active users; 30-day free trial

+Visual editor is the most capable here for non-technical users

+Bundles heatmaps, recordings and surveys, so you can see why a variant lost

+Server-side and mobile testing available on higher tiers

−No published pricing, so budgeting requires a sales conversation

−Cost scales with monthly active users and climbs quickly on high-traffic sites

Visit Wingify (formerly VWO) →

Optimizely

Best for: Large organisations running many experiments across teams

PricingCustom quote (Enterprise); part of a digital experience platform

+Best-in-class statistics, including sequential testing that permits safe early stopping

+Full-stack experimentation across web, mobile and server

+Serious governance: approvals, audit trails and program management

−Expensive enough to be an enterprise-only option in practice

−Implementation is a project, not an afternoon

Visit Optimizely →

PostHog

Best for: Product teams that want experiments, feature flags and analytics together

PricingFree tier, then usage-based paid plans; open source and self-hostable

+Experiments, feature flags, analytics and session replay in one tool, with one source of truth for events

+Genuinely generous free tier and transparent usage pricing

+Open source and self-hostable for data-sensitive teams

−No visual editor, so marketers need engineering help to build variants

−Statistics are solid but less sophisticated than Optimizely's

Visit PostHog →

GrowthBook

Best for: Data teams that want experiments on their own data warehouse

PricingFree to self-host and free cloud tier; paid plans above that

+Runs analysis directly against your data warehouse, so metrics match the numbers finance already trusts

+Open source with no vendor data collection, which simplifies privacy review

+Bayesian and frequentist engines with solid statistical rigour

−Requires an existing warehouse and the skills to use it

−No visual editor at all

Visit GrowthBook →

Convert Experiences

Best for: Mid-sized teams that want a visual editor and published prices without an enterprise contract

PricingFrom $399/mo (Growth, 100K tested users) or $299/mo billed annually; 15-day free trial, no card

+Published prices, which is rare among visual-editor testing tools

+Visual and code editors, multivariate, split URL and multipage tests on the entry plan

+Frequentist and Bayesian stats plus sequential testing

−Growth has tight limits on active projects and experiences, so busy programmes outgrow it

−Priced on monthly tested users, so the bill rises with traffic

Visit Convert Experiences →

What it is

An A/B testing platform splits traffic between variants, tracks a conversion goal, and reports whether the difference is statistically meaningful.

Modern platforms split into client-side tools that modify the page in the browser through a visual editor, and server-side or feature-flag tools that decide variants in your application code.

Client-side suits marketers changing copy and layout; server-side suits product teams testing functionality and avoids the flicker problem entirely.

Why it matters

Most opinions about what converts are wrong, including experienced ones, which is exactly why testing exists. The discipline also protects you from the opposite failure: shipping a redesign that quietly costs 8 percent of revenue and never knowing.

Beyond individual wins, a testing habit changes how a team argues, replacing the loudest voice with a measurement. That cultural effect is usually worth more than any single test result.

Key features to look for

Visual editor versus code

Whether a marketer can build a variant without an engineer. Powerful for velocity, but visual edits break when the underlying page changes, so it needs maintenance.

Server-side and feature flags

Deciding the variant in your backend removes flicker, works beyond the web, and lets you test functionality rather than just appearance.

Statistical approach

Frequentist or Bayesian, sequential testing support, and whether the tool actively stops you peeking at results early. Peeking is the most common way teams fool themselves.

Targeting and segmentation

Running a test on a specific audience, and segmenting results afterwards. Necessary, and also the easiest route to false positives if you slice until something looks significant.

Pricing model

Most price on monthly active or tested users, so cost scales with traffic rather than test count. Model this at your real volume before committing.

Pricing

Wingify (formerly VWO) and Optimizely are quote-only, PostHog has transparent usage pricing, GrowthBook leads with a free self-hosted option, and Convert Experiences publishes a price list.

Wingify, which now also sells AB Tasty after the two merged, prices Growth, Pro and Enterprise on monthly active users, does not publish rates, and offers a 30-day free trial. Optimizely sells an enterprise quote as part of a wider digital experience platform.

Convert Growth is $399/mo, or $299/mo billed annually, for 100K monthly tested users, and Pro is $599/mo, or $420/mo annually.

PostHog has a free tier, then usage-based paid plans, and is open source and self-hostable. GrowthBook is free to self-host with no user limits, plus a free cloud Starter plan for up to three users and a Pro plan at $40 per seat per month.

The cheapest credible entry is that free self-hosting or PostHog's free tier, and the bill jumps as monthly active or tested users rise, then again on an Optimizely quote.

Plan	Price	Best for
Wingify Growth	Custom quote, monthly active users	Small teams starting out; 30-day free trial
Wingify Pro	Custom quote, monthly active users	Businesses scaling their experimentation
Wingify Enterprise	Custom quote, monthly active users	Large teams and agencies with advanced requirements
Optimizely Enterprise	Custom quote	Part of a wider digital experience platform
PostHog paid plans	Usage-based paid plans	Generous free tier; open source, self-hostable
GrowthBook Pro	$40 per seat per month	Up to 30 users; free Starter cloud plan for 3 users and free self-hosting
Convert Growth	$399/mo, or $299/mo billed annually	100K tested users a month; visual editor, A/B, split URL and multivariate tests
Convert Pro	$599/mo, or $420/mo billed annually	100K tested users a month with higher project and experience limits

Mistakes to avoid

×Running tests without the traffic to resolve them. Calculate the required sample size before launching. If it exceeds your monthly traffic, you are not running an experiment, you are collecting anecdotes.

×Peeking and stopping early. Checking daily and stopping when a variant looks ahead massively inflates false positives. Either commit to the sample size or use a tool with sequential testing built for early stopping.

×Testing trivial changes. Button colours rarely move revenue. Test offers, pricing presentation, page structure and messaging, where the effect sizes are large enough to detect.

×Slicing results until something is significant. Segment analysis after a flat result is where most fake wins come from. Decide your segments in advance.

Expert tips

→Write the hypothesis and the required sample size before you build the variant. If you cannot state what you expect and why, the test will not teach you anything either way.

→Run tests for whole weeks. Traffic behaves differently on weekdays and weekends, and a partial week bakes that difference into your result.

→Keep a log of every test including the losers. The losing tests are where most of the durable learning about your audience lives.

→If you lack traffic for classic A/B testing, do qualitative research instead. Five user interviews beat an underpowered test that tells you nothing.

The bottom line

Marketing teams that want to build variants without engineering should look at Wingify (formerly VWO) first and budget for usage-based pricing, or at Convert Experiences if they want a published price.

Product teams already instrumenting events should use PostHog, where experiments, flags and analytics share one dataset and the free tier is real. GrowthBook is the pick when your warehouse is the source of truth, and Optimizely when experimentation is a company-wide programme. Before any of it, check whether your traffic can actually resolve a test.

Frequently asked questions

What is the best free A/B testing tool?

PostHog has the strongest free tier, bundling experiments, feature flags and product analytics with a monthly event allowance that covers real usage, and it can be self-hosted. GrowthBook is fully open source and free to self-host with no user limits, which suits teams that already have a data warehouse.

How much traffic do you need for A/B testing?

More than most teams expect. Detecting a 5 percent relative lift on a 3 percent baseline conversion rate typically requires tens of thousands of visitors per variant. Use a sample size calculator with your real baseline before you build anything. Below roughly 1,000 conversions a month, qualitative research usually beats testing.

What is the difference between client-side and server-side A/B testing?

Client-side changes the page in the browser after load, which enables visual editors but can cause a brief flicker of the original content. Server-side decides the variant in your application before rendering, which removes flicker, works beyond the web, and lets you test functionality rather than appearance. Server-side needs engineering involvement.

How long should an A/B test run?

Until it reaches the sample size you calculated in advance, and always in whole weeks so weekday and weekend behaviour are represented evenly. Two to four weeks is typical. Stopping early because a variant looks ahead is the most common way teams generate false positives, unless the platform explicitly supports sequential testing.

Sources

Prices and plan details come from each vendor's own pricing page, re-checked by the Toolradar pricing tracker.