Most “best A/B testing tools” lists rank everything against the same checklist, as if a 12-person ecommerce team and a 200-engineer SaaS company want the same thing. They do not. The right tool depends on who runs the tests, where your data lives, and whether you are changing pixels on a marketing page or shipping logic behind a feature flag.

So this guide is organised by what each tool is genuinely good at, with real pricing signals and the trade-offs that matter. The market also shifted hard in the last year. Google Optimize shut down on 30 September 2023, pushing a wave of teams onto paid platforms. In September 2025 OpenAI acquired Statsig for $1.1 billion in an all-stock deal. And on 20 January 2026, VWO and AB Tasty announced they were combining into a single digital experience optimisation business with more than $100 million in annual revenue. Two of the biggest names you would have compared separately a year ago are now one company.

Here is how the field actually breaks down.

What “best” depends on for your team

Before the tools, three questions decide most of the outcome.

  • Who builds the variations? Marketers and designers want a visual editor that changes a headline or hero image without touching code. Engineers want feature flags, SDKs, and tests defined in the codebase.
  • Where does conversion data live? Client-side tools track events in their own platform. Warehouse-native tools read straight from BigQuery, Snowflake, or Redshift, so your numbers match your existing dashboards.
  • What are you testing? Web pages and front-end UX point you towards a visual A/B platform. Pricing logic, onboarding flows, and model changes point towards server-side experimentation tied to feature flags.

Keep those three in mind and the categories below sort themselves out.

Enterprise web and feature experimentation

Optimizely is the platform most large experimentation programmes are benchmarked against. Web Experimentation handles A/B, multivariate, and multi-armed bandit tests, with Feature Experimentation covering server-side and feature-flag testing. Its statistics layer, the Stats Engine, runs sequential testing so you can monitor results continuously and call a winner once there is enough evidence, rather than waiting for a fixed end date. It also offers a fixed-horizon (frequentist) model aimed at regulated environments and teams with predictable traffic, alongside Bayesian options. You can read how the methodology works in Optimizely’s own Stats Engine explainer.

The catch is cost and scope. Optimizely is sold as part of a broader digital experience platform, contracts are annual, and pricing is custom and firmly enterprise. It is the right answer when many teams run experiments in parallel and you need governance, statistical rigour, and deep integrations. It is overkill for a single growth team running a handful of tests a month.

Choose Optimizely if you have a mature, multi-team programme and budget to match.

The all-in-one mid-market platform

VWO has been the default “everything in one dashboard” choice below enterprise pricing for years. One subscription covers client-side and server-side testing, multivariate tests, plus behavioural insight tools like heatmaps, session recordings, form analytics, and on-page surveys. That bundling is why so many ex-Google Optimize users landed here: you get a competent testing engine and the qualitative tools to figure out what to test next, without buying two products.

Pricing runs across Growth, Pro, and Enterprise tiers with a free option to explore the platform. Public list pricing is not published on most plans, but VWO has historically been reachable for smaller teams in a way Optimizely is not, and it has added an AI assistant, Copilot, that drafts test ideas and variations.

The 2026 wrinkle is the AB Tasty combination. AB Tasty brings a strong visual editor, AI-driven widget building, and EmotionsAI segmentation, and it is well established in European markets. On its own it sells through custom quotes rather than public list prices. As the two integrate under shared leadership, expect overlapping features and a single roadmap. If you are evaluating either today, ask directly how your plan, contract, and support fit into the merged product before you sign a multi-year deal.

Choose VWO (or the combined platform) if you want testing and behavioural analytics together and you are not at enterprise scale.

Privacy-first testing

Convert built its reputation on flicker-free experiments and a privacy stance that predates most of its rivals. It uses only first-party cookies, is GDPR, CCPA, and LGPD aligned, and supports cookieless testing through a Bring Your Own ID approach, so you can keep a visitor in the same variation using your own login or session identifiers. Pricing starts at $399 per month and is based on monthly tested users, with no feature gating between plans.

That combination matters most for teams in regulated sectors, for European and UK sites with strict consent requirements, and for agencies that need to promise clients clean data handling. You give up some of the personalisation depth of the larger suites, but you get a focused testing tool that is straightforward about what it does with visitor data.

Choose Convert if privacy compliance and predictable per-user pricing are non-negotiable.

Warehouse-native and open source

GrowthBook is the pick when experiment data should stay in your own warehouse. It is open source under the MIT licence and warehouse-native, reading metrics directly from BigQuery, Snowflake, Redshift, or ClickHouse rather than holding them in a separate platform. That means your experiment results reconcile with the dashboards your data team already trusts.

The pricing model is unusually friendly. The self-hosted edition is free with unlimited users, flags, and experiments. The hosted Starter plan is free for up to three users. Pro is $40 per user per month and adds the visual editor, sequential testing, CUPED variance reduction, and sticky bucketing. Because it charges per seat rather than per monthly active user or per event, costs stay predictable as traffic grows.

Choose GrowthBook if you have a data team, a warehouse, and want full ownership of your numbers without per-event billing.

All-in-one for product teams

PostHog bundles experiments, feature flags, product analytics, session replay, and surveys into one open-source suite, which makes it a strong fit for product and engineering teams who want analytics and testing in the same place. Its experiments consume from the same feature-flag request quota rather than billing as a separate line item, and the free tier is genuinely generous: 1 million feature-flag requests a month at no cost, with transparent usage-based pricing after that. PostHog publishes its full rates openly, which you can check on the PostHog pricing page.

Choose PostHog if you want product analytics, flags, and experiments in a single tool and prefer usage-based, transparent pricing.

A note on Statsig: it remains a serious large-scale experimentation platform, pairing feature flags with real-time analytics. Since the OpenAI acquisition it continues to operate and serve existing customers, but if you are signing a multi-year contract it is worth asking about long-term roadmap and independence given the new ownership.

Quick decision guide

  • Large multi-team programme, budget available: Optimizely.
  • Testing plus heatmaps and recordings, mid-market: VWO and the combined AB Tasty platform.
  • Strict privacy or consent requirements: Convert.
  • Engineering team, warehouse, data ownership: GrowthBook.
  • Product team wanting analytics, flags, and tests together: PostHog.

Whichever you shortlist, the tool is the smaller half of the problem. A clear hypothesis, enough traffic to reach significance, and the discipline to not peek early matter more than the brand on the dashboard. If you are still firming up the statistics side, read our guide to how to calculate A/B test sample size before you commit to a platform.

Frequently asked questions

What is the best A/B testing tool for a small startup? For most small teams, start with GrowthBook (free self-hosted or a free Starter plan) if you have engineering and a data warehouse, or PostHog if you want analytics, flags, and experiments bundled with a generous free tier. Both let you run real tests without a five-figure contract.

How much do A/B testing tools cost in 2026? It ranges enormously. Open-source self-hosting (GrowthBook) and generous free tiers (PostHog) can cost nothing to start. Convert begins at $399 per month. VWO and the AB Tasty side sit in the mid-market with custom quotes, and Optimizely is enterprise-priced with annual contracts. Match the spend to your traffic and the number of people running tests.

Is there a good free A/B testing tool after Google Optimize shut down? Yes. Google Optimize closed on 30 September 2023, but GrowthBook’s self-hosted edition is free and open source, GrowthBook’s hosted Starter plan is free for up to three users, and PostHog offers 1 million free feature-flag requests a month. None is a drop-in clone of Optimize, but each covers core A/B testing at no cost.

What is the difference between client-side and server-side A/B testing? Client-side testing changes what the browser renders, which suits marketers editing headlines, layouts, and images through a visual editor. Server-side testing changes application logic before it reaches the user, which suits engineers testing pricing, onboarding flows, or backend behaviour through feature flags and SDKs. Many platforms now do both.

Did VWO and AB Tasty really merge? Yes. On 20 January 2026 the two companies announced they were combining into a single digital experience optimisation business backed by Everstone Capital, with more than $100 million in annual revenue and over 4,000 customers. If you are evaluating either, ask how your specific plan and contract fit the merged roadmap.

Which tool is best for privacy and GDPR compliance? Convert is the most privacy-focused of the mainstream options, using only first-party cookies, supporting cookieless testing via its Bring Your Own ID approach, and aligning with GDPR, CCPA, and LGPD. Warehouse-native and self-hosted tools like GrowthBook also help because experiment data stays inside your own infrastructure.