---
title: "Best A/B Testing Tools in 2026, Compared"
description: "Statsig, GrowthBook, PostHog, LaunchDarkly, Optimizely and more compared on free tiers, pricing model and stats engine, checked against each vendor's own page."
canonical_url: "https://builderscamp.com/guides/tools/best-ab-testing-tools"
date_published: "2026-10-23"
date_modified: "2026-10-23"
author: "Andre Albuquerque, Joy Chatterjee"
publisher: "Builders Camp"
guide_class: "tools"
---

# Best A/B Testing Tools in 2026

**TL;DR:** GrowthBook is the best A/B testing tool for most product teams in 2026: free for up to 3 users on its cloud plan, $40 per seat per month on Pro, and open source if you want to self-host. Pick Statsig when you want experiments, flags and analytics on one usage-based bill, PostHog when one free account should also cover replay and surveys, and Optimizely or Wingify when marketing runs page tests in a visual editor.

GrowthBook is the default pick for a product team running its first serious experimentation program, and the reason is its pricing model rather than any single feature. [GrowthBook's pricing FAQ](https://www.growthbook.io/pricing) puts it plainly: "Teams pay for the people who run experiments and manage flags, not for the end users who see them". That matters because traffic grows faster than the team that runs tests, and a per-user bill grows with it.

The other side of the market prices on usage and is generous at the bottom. [PostHog's pricing page](https://posthog.com/pricing) states that 97% of companies use PostHog for free, with 1 million feature flag requests a month included and experiments billed through those flag requests. [Statsig's free Developer plan](https://www.statsig.com/pricing) includes 2 million events a month. A team with modest traffic can run a real experiment program on any of the three without paying.

Last verified: 23 October 2026. Every price and plan limit below comes from the vendor's own pricing page on that date.

## How were these A/B testing tools chosen?

Each tool had to meet four criteria. It runs randomized experiments with a statistical readout, not just a visual editor. It publishes its plans on its own site, or says plainly that pricing is quoted. It is actively sold as of the verification date. And it covers at least one of the two jobs product teams have: testing inside the product (flags plus metrics) or testing marketing pages (visual editor plus conversion goals). Tools were not ranked from third-party roundups. One former default is missing on purpose: [Google's Analytics Help page](https://support.google.com/optimize/answer/12979939?hl=en) states that Google Optimize and Optimize 360 are no longer available as of September 30, 2023.

| Tool | Best for | Free tier | Paid from | Standout |
|---|---|---|---|---|
| GrowthBook | Product teams that want flags and tests without per-user pricing | Up to 3 users (cloud); unlimited users self-hosted | $40/seat/month (Pro) | Open source, warehouse-native |
| Statsig | Teams that want experiments, flags and analytics on one bill | 2M events/month | $150/month (Pro, 5M events) | Sequential testing, CUPED, switchback tests listed |
| PostHog | Early teams that want one free account for flags, tests, analytics and replay | 1M flag requests/month | Pay as you go | Experiments billed with feature flags |
| LaunchDarkly | Engineering-led release management with experiments attached | $0 Developer plan, 100K experimentation MAU/month | Pay as you go (Foundation) | Unlimited seats, no per-seat fees |
| Optimizely | Large marketing and digital teams | None listed | Quoted per plan | Web, feature and server-side testing in one suite |
| Wingify (VWO and AB Tasty) | Marketing teams testing pages in a visual editor | Free trial | Quoted by monthly active users | Visual and code editor, SRM detection on Pro |
| Datadog Experiments (formerly Eppo) | Data teams with a warehouse and their own metric definitions | None listed | Demo-based | Warehouse-native, contextual bandits |

## Why is GrowthBook the best A/B testing tool for most product teams?

GrowthBook combines feature flags, experiments and basic product analytics, and it runs its statistics against your own data warehouse. [GrowthBook's pricing page](https://www.growthbook.io/pricing) lists a free cloud Starter plan for up to 3 users and 1 project with unlimited feature flags, experiments and traffic, and a Pro plan at $40 per seat per month for up to 30 users. The self-hosted open source version is free with unlimited users, according to the same page's FAQ.

Pro is where the statistics a PM actually needs show up: the Pro plan lists multi-arm bandits, and its FAQ adds sequential testing, CUPED variance reduction and sticky bucketing. GrowthBook connects directly to Snowflake, BigQuery, Redshift, Databricks and ClickHouse, so experiment metrics use the same definitions as the rest of your reporting instead of a second copy of the truth.

The trade-off is setup. Warehouse-native means someone has to connect the warehouse and define metrics before the first result is worth reading. GrowthBook offers a managed warehouse option for teams without one, but a team with no data person should expect a slower first week than with PostHog.

## When is Statsig the better choice?

Statsig is the better choice when you want experiments, flags, product analytics and session replay on one usage-based bill with unlimited seats. [Statsig's pricing page](https://www.statsig.com/pricing) lists a free Developer plan with 2 million events a month, unlimited flag and config checks and 50,000 session replays a month. The Pro plan is $150 a month with 5 million events included, then $0.05 per 1,000 events.

The stats engine is the main reason data-literate teams pick it. The same page lists both Bayesian and frequentist methods, CUPED, Bonferroni correction, sequential testing to prevent early peeking, switchback tests and non-inferiority tests, plus holdouts to measure the cumulative effect of many shipped changes. If your team keeps asking whether it can stop a test early, sequential testing is the honest answer, and the [guide to stopping an A/B test early](https://builderscamp.com/guides/other/can-you-stop-an-ab-test-early) explains why peeking at a fixed-horizon test inflates false positives.

## Is PostHog good enough for A/B testing on its own?

For an early product, yes. PostHog's experiments run on its feature flags, and [PostHog's pricing page](https://posthog.com/pricing) includes 1M flag requests, 1M analytics events and 5K session recordings free every month, with unlimited team members on the free plan. Usage beyond that is pay as you go, and most products can set a billing limit so a traffic spike does not turn into a surprise bill.

The appeal is having the test result, the funnel and the replays in one place. When a variant wins, you can watch sessions from that variant in the same tool, which is the fastest way to understand why. The limit is depth: teams that need warehouse-native metrics, holdouts across many experiments or formal experiment review workflows tend to graduate to GrowthBook, Statsig or Datadog Experiments.

## Is LaunchDarkly an A/B testing tool or a feature flag tool?

LaunchDarkly is a feature management platform first, with experimentation included. [LaunchDarkly's pricing page](https://launchdarkly.com/pricing/) lists a $0 Developer plan with unlimited seats, unlimited feature flags and 100K experimentation MAU a month at no additional charge. The paid Foundation plan is pay as you go, with no platform or seat fees, at $10 per service connection a month and $8.33 per 1,000 client-side MAU a month billed yearly.

Pick LaunchDarkly when engineering already owns releases and wants approval requests, scheduled flag changes and multi-step release workflows, and the PM wants experiments attached to the same flags. Pick a dedicated experimentation tool when the experiment readout, not the release process, is the thing you are buying.

## Which A/B testing tool is best for marketing pages?

Optimizely and Wingify are built for marketing and digital teams that test landing pages, pricing pages and checkout flows. [Optimizely's plans page](https://www.optimizely.com/plans/) says "Every Optimizely plan is individually packaged" and lists A/B, multi-page, feature and server-side testing, a proprietary Stats Engine and warehouse-native analytics in its experimentation product. There is no public price; expect a sales conversation.

VWO now sells under the Wingify name after merging with AB Tasty, as the banner on [Wingify's pricing page](https://vwo.com/pricing/) states. Its testing plans start with a Growth tier that includes a visual (WYSIWYG) and code editor and unlimited experiments, variations and metrics; Pro adds guardrail metrics, sample ratio mismatch detection and multivariate testing. Pricing is based on monthly active users and quoted after a demo, with a free trial available.

## When does a warehouse-native tool like Datadog Experiments make sense?

When a data team already owns metric definitions in a warehouse and wants experiment analysis to run on them directly. [Eppo's site](https://www.geteppo.com/) now states that Eppo has been acquired by Datadog and is now Datadog Experiments. The product description stresses a zero-copy, warehouse-native architecture, CUPED++ to shorten experiment runtime, and contextual bandits for personalization. It is sold through a demo, with no public price. If your team does not yet have a warehouse and an analyst, this is the wrong first tool.

## Which A/B testing tool is best for a solo founder?

PostHog, because one free account covers flags, experiments, analytics and replay, and nothing has to be connected to a warehouse. A solo founder rarely has the traffic to run many experiments at once, so the realistic job is a handful of tests a quarter plus watching what users do. If you later want a dedicated experimentation tool, GrowthBook's free Starter plan covers up to 3 users at no cost.

## Which A/B testing tool is best for a startup with a data team?

GrowthBook or Statsig. Choose GrowthBook if your metrics already live in a warehouse and you want seat-based pricing that does not grow with traffic. Choose Statsig if you want one vendor to host events, flags, analytics and replay, and you are comfortable with usage-based pricing above the free 2 million events.

## Which A/B testing tool is best for an enterprise team?

The answer turns on who runs the tests. If engineering owns releases across many services, LaunchDarkly's Enterprise plan adds custom roles, approval workflows and scheduled flag changes. If a central data team owns metrics in a warehouse, Datadog Experiments or GrowthBook Enterprise fits. If marketing and digital teams run most tests on web pages, Optimizely or Wingify is the usual choice.

## Do you need an A/B testing tool at all?

Not always, and this is the strongest objection to buying one early. An A/B test only answers a question when enough users pass through both variants to detect the effect you care about. If one test would take a quarter to reach a result, the tool is not the bottleneck. Three cheaper moves teach more at low traffic:

- Run a staged rollout behind a free feature flag and watch the guardrail metrics, so you catch damage without claiming a measured lift.
- Run a usability test to find the obvious problems before you spend traffic on them; the [usability testing vs A/B testing comparison](https://builderscamp.com/guides/comparison/usability-testing-vs-ab-testing) covers when each answers the question.
- Write the hypothesis and the decision rule first, using the four steps in [how to run an A/B test](https://builderscamp.com/guides/other/how-to-run-an-ab-test), so the tool you pick later has a clear job.

When traffic is uneven or the cost of showing a losing variant is high, a bandit may suit better than a fixed split; the [multi-armed bandit vs A/B testing guide](https://builderscamp.com/guides/comparison/multi-armed-bandit-vs-ab-testing) walks through that trade-off. Experiment results also only mean something if the metric underneath is instrumented correctly, which is why the [best product analytics tools](https://builderscamp.com/guides/tools/best-product-analytics-tools) usually come before the testing tool, and why [session replay tools](https://builderscamp.com/guides/tools/best-session-replay-tools) are worth pairing with any test to see why a variant won.

## What does the A/B Testing for Product Managers bootcamp cover?

Picking a tool is the easy half; reading the result correctly is the hard half. Builders Camp's [A/B Testing for Product Managers](https://builderscamp.com/bootcamps/ab-testing-for-product-managers) bootcamp is about designing experiments, choosing metrics, interpreting results and avoiding false confidence. It runs over 1 week with 2 live sessions and 8 microlessons. For a reading list to go with it, see the [best A/B testing resources for product managers](https://builderscamp.com/guides/resources/best-ab-testing-resources-for-product-managers), and for a ready-made format to log ideas, the [experiment backlog template](https://builderscamp.com/guides/templates/experiment-backlog-template).

## Frequently asked questions

### What is the best free A/B testing tool?

For a product team, GrowthBook and Statsig have the most usable free plans. GrowthBook's cloud Starter plan is free for up to 3 users with unlimited experiments and traffic, and its self-hosted open source version has unlimited users. Statsig's free Developer plan includes 2 million events a month and unlimited flag checks. PostHog is the pick if you also want analytics and session replay in the same free account.

### What is the difference between an A/B testing tool and a feature flag tool?

A feature flag decides which users see a piece of code; an A/B test is a feature flag plus a metric and a statistical readout. Most product-side tools now ship both on the same SDK: Statsig, GrowthBook, PostHog and LaunchDarkly all sell flags and experiments together. Marketing-side tools like Optimizely's web experimentation and Wingify's visual editor start from the page instead of the code.

### Did Google Optimize get replaced by anything?

Google's Analytics Help page states that Google Optimize and Optimize 360 are no longer available as of September 30, 2023, and there is no single replacement. Teams that ran marketing page tests in a visual editor usually move to a web experimentation tool such as Optimizely or Wingify. Teams that test inside the product usually move to a flag-based platform such as GrowthBook, Statsig or PostHog, which also covers the server side.

### How much traffic do I need before an A/B testing tool is worth paying for?

Enough to reach a readable result on the metric you care about, which depends on your baseline conversion rate and the smallest effect worth detecting. Run a sample size calculation before you pick a plan. If one test would take months to finish, a usability test or a staged rollout behind a free feature flag will teach you more for the same effort.

### Is Eppo still available?

Eppo's own site now says Eppo has been acquired by Datadog and is now Datadog Experiments. The product keeps its warehouse-native design and its contextual bandits, sold through Datadog with demo-based pricing.

### What happened to VWO?

VWO and AB Tasty merged, and VWO's pricing page now carries the Wingify name. Plans are quoted by monthly active users, with a free trial and a demo for exact pricing.

### Should a product manager pick the tool or leave it to engineering?

Decide together, because the two roles care about different parts of it. Engineering cares about SDK support, latency and where flags are evaluated. The PM cares about metric definitions, the stats engine, guardrail metrics and whether results can be read without an analyst. A tool that only one side can use ends up running fewer tests.

## Sources

- [Statsig: Pricing](https://www.statsig.com/pricing)
- [GrowthBook: Pricing](https://www.growthbook.io/pricing)
- [PostHog: Pricing](https://posthog.com/pricing)
- [LaunchDarkly: Pricing](https://launchdarkly.com/pricing/)
- [Optimizely: Plans and pricing](https://www.optimizely.com/plans/)
- [Wingify (VWO and AB Tasty): Pricing and plans](https://vwo.com/pricing/)
- [Eppo: Eppo is now Datadog Experiments](https://www.geteppo.com/)
- [Google Analytics Help: Google Optimize sunset](https://support.google.com/optimize/answer/12979939?hl=en)

## How this guide was made

Researched from Builders Camp's bootcamp, track and masterclass material and the sources listed on this page, drafted with AI, and fact-checked against every source cited.