15 Best A/B Testing Tools for Higher Website Conversions

Most teams pick an A/B testing tool the same way: they search for a ranked list, choose the one with the longest feature page, and then run two versions of a page against each other. That approach fails for a predictable reason. The tool is rarely the bottleneck. Traffic, test ideas and who actually builds the variations decide whether the tool pays off. A tool that suits a product team with engineers will frustrate a marketer who just wants a better headline.

This article is for founders and website owners who want more signups and need to know which A/B testing tool to use for their website's conversions, how much it will really cost, and whether a classic A/B tool is the right approach at all. The short answer: marketers on a website should start with a visual-editor tool such as Crazy Egg, VWO, Convert Experiences or Unbounce. Product teams with developers should look at GrowthBook, Statsig, PostHog or LaunchDarkly. Large organisations buy Optimizely or Kameleoon. If your traffic comes from many different sources, ask whether one winning page for everyone is the goal, and read the section on per-visitor approaches before you buy.

What Should an A/B Testing Tool Do?

An A/B testing tool shows different versions of a page, message or feature to different visitors, measures which version produces more of a chosen action, and helps you keep the winner. For a website owner, that action is usually a signup, a trial start or a purchase. The tool handles traffic splitting, tracking and reporting so you do not build that plumbing yourself.

Tools differ in the kinds of test they run:

• A/B test: Two versions of one page or element, with traffic split between them. This is the default and the right place to start.
• Split-URL test: Two separate pages at different addresses, with traffic divided between them. Use it when the variants are redesigns rather than small edits.
• Multivariate test: Several elements changed at once, such as headline, image and button, to see which combination wins. It needs much more traffic than an A/B test. The guide to multivariate testing in marketing covers when it is worth it.
• Bandit test: Traffic shifts automatically toward the better-performing version while the test runs. It trades some statistical clarity for fewer lost conversions. See the explainer on the multi-armed bandit approach.

What a tool cannot do

A tool cannot supply ideas, and it cannot create traffic. If you have no reasoned guess about why visitors hesitate, you will test random changes and learn little. Write a clear A/B testing hypothesis before you build anything. If your site gets few visitors, many tests will never reach a reliable result, however good the software.

Practical rule: A testing tool measures your ideas. It does not replace having them, and it does not replace traffic.

How Were These Tools Selected and Grouped?

The tools below come from the products that appear repeatedly in the ranking comparison pages for this query, including roundups from Crazy Egg, Convert and Optibase. We did not run hands-on tests of these products. The descriptions rely on how those pages and the vendors position each tool, so treat them as a shortlist to verify, not a verdict.

The ranking pages commonly compare tools on use case, test types, analytics, integrations and pricing. Those five criteria drive the choice:

• Use case: Decide whether you are testing marketing pages, product features or both. Page tests need a visual editor. Feature tests need code-level flags.
• Who does the work: A marketer can run a visual-editor tool alone. A developer-first tool means you wait for an engineer each time. Setup effort follows this split.
• Test types: Check that the tool supports the format you need: A/B, split-URL, multivariate or bandit. Listings rarely agree, so confirm on the vendor's page.
• Analytics and integrations: Results should land in the analytics you already trust. Product teams often want the testing inside their product analytics or data warehouse.
• Pricing model: Plans are flat monthly, per seat, usage-based or quote-based. Each behaves differently as you grow.
• Traffic needs: Low-traffic sites should favour cheap, simple tools and bigger changes. High-traffic sites can afford the statistical power of full platforms. The minimum detectable effect explains how much traffic a given test needs.

A note on prices. Vendor prices differ across the sources used here, and the pages dated in 2026 report different plan names and amounts for the same product. Treat every price below as a range from a third-party listing and confirm it on the vendor's own pricing page before you decide.

1. Crazy Egg: Best for Small Marketing Teams That Want Testing Plus Behaviour Data

Best For

Marketers and site owners who want a low-effort CRO tool. Crazy Egg positions itself for conversion rate optimisation, so the work sits with the marketing side, not engineering.

What Stands Out

The comparison listing in Crazy Egg's own roundup states that it supports A/B testing, split-URL testing, and multivariate or bandit tests. That is the widest stated test-type coverage of any budget tool in the research.

Pricing Model

A/B testing starts at $99/month, according to Crazy Egg, which also lists plans at $99, $249 and $599 per month. Treat that as a reference point for a small-team budget, not a quote.

Drawbacks

The figures come from the vendor's own comparison page, so they favour its own product. The $599 top plan is a large step up for a small site. Confirm which plan includes the test types you need.

2. VWO: Best for Mid-Market CRO Teams Running a Steady Test Programme

Best For

Marketing and CRO teams that run tests every month and want a dedicated platform with a visual editor. VWO is positioned for CRO or mid-market testing.

What Stands Out

VWO is among the most widely listed names in the category, so documentation, agencies and community knowledge are easy to find. If you are comparing it with an enterprise rival, the VWO vs Optimizely breakdown covers the head-to-head.

Pricing Model

Sources disagree. Some list VWO as quote-based. Others show a $199/month starting price, according to Toolradar, or a $299/month Growth plan, according to Optibase. The gap likely reflects different products or plans. Use the range as a planning bracket and ask the vendor for the exact plan.

Drawbacks

Conflicting public pricing makes budgeting harder. A mid-market platform is more than a small, low-traffic site needs.

3. Convert Experiences: Best for Growth Teams That Care About Privacy

Best For

Growth and CRO teams that want a testing tool positioned around privacy-conscious experimentation.

What Stands Out

Convert positions Experiences for growth teams and publishes its own comparison of the field. Its listing also mentions full-stack testing, which extends beyond page edits.

Pricing Model

Convert lists $299/month billed annually or $399/month billed monthly for 100,000 tested users, according to Convert. Another listing puts full-stack testing at $420/month, according to Optibase. Use these as reference points. Tested-user limits shape what you pay as traffic grows.

Drawbacks

The entry price is higher than the budget tools in this list. Annual billing lowers the monthly rate but commits you for a year before you know the tool fits.

4. AB Tasty: Best for Teams Combining Testing With Personalisation

Best For

Marketing teams that want testing and personalisation in one platform and are willing to talk to sales.

What Stands Out

AB Tasty is positioned for personalisation and testing, so it sits between a pure A/B tool and a personalisation suite. That helps if you already plan to show different content to different segments.

Pricing Model

Custom or quote-based, according to the listings in Crazy Egg and Optibase. No public starting price was found.

Drawbacks

Without a public price you cannot compare it quickly, and quote-based plans usually suit larger budgets. Segment-based personalisation still depends on you defining the segments and writing the variations.

5. Unbounce: Best for Landing-Page Testing Without a Developer

Best For

Marketers who build landing pages for campaigns and want testing inside the page builder. Unbounce is positioned for landing-page testing.

What Stands Out

The testing lives where the pages are built, so one tool covers building and testing. For more on that category, see the roundup of best landing page tools.

Pricing Model

Listings are inconsistent: $29/month in one result, A/B testing from $149/month in another, and $74/month in a third, according to Crazy Egg and Optibase. The spread shows why you must check the plan that includes testing.

Drawbacks

It tests pages built in Unbounce. If your main site lives elsewhere, you test a separate landing page, not your whole site.

6. GrowthBook: Best for Product Teams That Want Open Source and Warehouse-Native Analysis

Best For

Startup-to-SaaS teams with developers and a data warehouse. GrowthBook is positioned for warehouse-native experimentation.

What Stands Out

It is listed as free and open source, so a team can start without a contract. Warehouse-native means results are computed from data you already store, which keeps experiment analysis next to the rest of your reporting.

Pricing Model

Free and open source, with paid Pro pricing at $40 per seat per month, according to Crazy Egg and Convert. Per-seat pricing grows with the number of people who need access, so count your seats first.

Drawbacks

It is built for teams with engineering support, so a marketer working alone will struggle. A warehouse-native setup assumes you already have a warehouse worth querying.

7. Statsig: Best for Product Teams That Want a Free Starting Tier

Best For

Product and engineering teams that want experimentation with a free plan to start.

What Stands Out

Statsig is listed with a free Developer plan, which lets a team try experiments before paying. It is positioned for product teams, not for marketers editing page copy.

Pricing Model

A free Developer plan and a Pro plan from $150/month, according to Toolradar and Convert. Use the free plan to judge fit before committing to Pro.

Drawbacks

Developer-led setup means marketing tests wait for engineering time. Plan limits on the free tier are worth checking against your traffic.

8. LaunchDarkly: Best for Feature Flags and Controlled Rollouts

Best For

Engineering-led teams that already ship behind feature flags and want experiments on the same system.

What Stands Out

LaunchDarkly is positioned for product experimentation and feature flags. A feature flag lets you turn a change on for a share of users without a new release, which is the foundation of server-side testing.

Pricing Model

A free plan and usage-based pricing, according to Crazy Egg. Usage-based pricing tracks how much you serve, so costs rise with traffic.

Drawbacks

It is a developer tool. It is not the quickest way to test a homepage headline, and a site owner with no engineer will find it more than needed.

9. PostHog: Best for Teams That Want Experiments Inside Product Analytics

Best For

Product teams that want testing, feature flags and analytics in one place.

What Stands Out

PostHog integrates experimentation with product analytics, so you see test results beside funnels and events without exporting data. It also appears in Webflow's testing roundup.

Pricing Model

Free or usage-based, with one listing citing free allowances for events or feature-flag requests, according to Crazy Egg and Convert. Check the allowance against your monthly event volume.

Drawbacks

Usage-based pricing is hard to forecast for a growing site. Setup suits teams comfortable adding tracking code.

10. Mixpanel: Best for Teams Already Analysing Product Behaviour There

Best For

Teams that already use Mixpanel for analytics and want to add experiments to the same data.

What Stands Out

The value is continuity: you keep your events, cohorts and reporting. Mixpanel is a product analytics tool first, so it fits when analytics is already in place.

Pricing Model

A free plan and a paid Growth plan from $20/month, according to Toolradar. Confirm what the Growth plan includes for experimentation.

Drawbacks

If you do not already use Mixpanel, adopting an analytics platform just to test is a large step. It is not a visual page editor for marketers.

11. Optimizely: Best for Enterprises With Large Traffic and Budgets

Best For

Large organisations that need a full experimentation platform with governance and dedicated support.

What Stands Out

Optimizely is positioned for enterprise experimentation and is often the benchmark other tools are compared with. See Optimizely alternatives if you want the cheaper options.

Pricing Model

Quote-based, with one result indicating $50,000+ per year, according to Crazy Egg and Optibase. Use that as a sense of scale. Confirm with the vendor.

Drawbacks

The cost and sales process rule it out for most small sites. A founder with modest traffic would pay for capacity they cannot use.

12. Kameleoon: Best for Mid-Market and Enterprise Teams Wanting AI-Assisted Testing

Best For

Mid-market or enterprise teams that want AI-assisted testing and are comfortable with a sales conversation.

What Stands Out

Kameleoon is positioned for AI-assisted testing, which suits teams that want help prioritising or targeting experiments.

Pricing Model

Sources conflict: custom pricing in one result and a $495/month starter plan in another, according to CostBench and Optibase. If a starter plan exists, it is still above most budget tools.

Drawbacks

Unclear public pricing and a mid-market focus make it a poor first tool for a small site. Verify what the AI features do before paying for them.

13. Zoho PageSense: Best for Small Sites That Want a Free Plan

Best For

Small businesses, especially those already in the Zoho ecosystem, who want to start testing at no cost.

What Stands Out

It has a free plan for up to 5,000 monthly users, according to Convert. That makes it one of the lowest-risk ways to try testing.

Pricing Model

Free up to that limit, with paid plans from $12/month billed annually, according to Convert. Use it as a floor for the category.

Drawbacks

A 5,000-user cap on the free plan will be hit by growing sites. Cheap plans often mean fewer advanced features, so check test-type support.

14. Optibase: Best for WordPress and Webflow Sites With No Developer

Best For

Site owners on WordPress, Webflow or page builders who want no-code testing.

What Stands Out

Optibase is listed as a no-code option for WordPress, Webflow and page builders. It also publishes a comparison of 14 platforms, which is a useful cross-check. Note that this is the vendor's own listing.

Pricing Model

A free plan and paid plans starting at $69/month, according to Optibase.

Drawbacks

It is built around those site platforms, so it fits less well on a custom-coded app. The no-code trade-off is less control over complex or server-side tests.

15. Varify.io: Best for the Tightest Budgets

Best For

Very small sites that want a low-cost no-code tool.

What Stands Out

It is listed as a budget no-code option with a free plan, so you can start cheaply and see whether testing fits your workflow.

Pricing Model

Approximately $33/month, with a free plan, according to Optibase. The figure is approximate, so confirm it.

Drawbacks

Budget tools usually offer fewer integrations and less support. Little independent information exists in the research, so test the free plan before you build your process around it.

How Do the Tools Compare Side by Side?

The table summarises the listings above. Prices come from vendor and third-party snippets and are ranges, so confirm them on each vendor's page.

Test types. Only Crazy Egg's listing explicitly names A/B, split-URL, and multivariate or bandit support. For every other tool, check the vendor's documentation for the test types you need before you buy. Do not assume multivariate or bandit support is included on a lower plan.

Is a Classic A/B Testing Tool Enough to Increase Signups?

A classic A/B testing tool is enough when one page can work well for all visitors. It stops being enough when visitors arrive from different places with different expectations. Your goal is more signups, not a tidy test report. A tool that finds one winning page serves the average visitor and may underserve everyone else.

The limit of one winner for everyone

A/B testing isn't a search for the best page. It's a search for the best page on average. A visitor who arrives from a search for a specific problem and one who arrives from a social post carry different questions, yet the winning variation reaches both. Most websites already behave this way: they show the same content to every visitor, wherever they come from.

How visit signals change the approach

Polish is our product, so read this section with that in mind. Polish is an AI agent built for founders and website owners who want to increase signups. It reads visit signals to automatically rewrite headlines, copy and calls to action for each visitor. It continuously tests variations on live traffic and keeps the versions that generate conversions.

The difference from a classic tool is who does the work. With a classic tool you write the hypothesis, build the variants, wait for significance and ship the winner. With Polish the rewriting and the continuing test happen as an agent task on live traffic. For background, see the guides on AI-powered personalization and AI website optimization.

A worked example

Picture a founder with a signup page and three traffic sources: search, a newsletter and paid ads. Before: the founder runs a classic A/B test on the headline, waits several weeks, and ships one winner to everyone. Search visitors arrive with a specific problem in mind, newsletter readers already know the brand, and ad clicks came from a promise in the ad. All three see the same words. After: a per-visitor approach reads where each visit came from and rewrites the headline, copy and call to action to match, then keeps testing which versions convert. The founder no longer picks one page for everyone.

When a classic tool is still the better pick

Choose a classic tool when you test product features behind flags, when a regulated or brand-controlled page must stay identical for every visitor, or when your team wants to control every word and read the statistics itself. Classic tools also suit the question "did this redesign beat the old page?". Per-visitor rewriting answers a different question: what should each visitor see?

Practical rule: If all visitors want the same thing, test a page. If they want different things, test the message for each visitor.

Which A/B Testing Tool Fits Your Situation?

Pick by your situation first, then by feature list. Each case below includes a first step.

• Low traffic: Choose a cheap or free plan such as Zoho PageSense, Varify.io or Optibase, and test large changes only. Small tweaks will not reach a reliable result. First step: calculate the minimum detectable effect your traffic can support.
• No developer: Choose a visual-editor tool: Crazy Egg, VWO, Convert Experiences or Unbounce for marketers, or Optibase on WordPress and Webflow. First step: run one headline test on your highest-traffic page.
• Product analytics already in place: Choose the experimentation inside it: PostHog, Mixpanel, or GrowthBook if you have a warehouse. First step: run one experiment on data you already collect.
• Feature flags and engineering-led: Choose LaunchDarkly or Statsig. First step: put one change behind a flag and split it.
• Enterprise: Choose Optimizely or Kameleoon, and ask for quotes from both. First step: write your requirements on governance, traffic and integrations before the sales calls.
• Many traffic sources and a signup goal: Consider a per-visitor approach such as Polish alongside, or instead of, a classic tool. First step: split your signups by source and see where one page underperforms.

Your Next Steps

Work through these in order:

1. Name the goal. Write down the action you want: signups, trials or purchases.
2. Check your traffic. Count monthly visitors per page. If it is low, plan for big changes and a free tier.
3. Decide who builds variants. A marketer points to a visual editor. A developer points to flags.
4. Shortlist two or three tools. Use the situation guide above.
5. Confirm prices and test types. Open each vendor's pricing page, since listings conflict, and check split-URL, multivariate and bandit support.
6. Write one hypothesis. Use the A/B testing examples for reference on what a clear test looks like.
7. Run one test, then review. Check whether the page, the source mix or the traffic was the limit.

The tool question and the signup question are connected. Classic testing tools give you control, with a cost in setup, traffic and waiting time. Marketing-led tools lower the engineering effort, product-led tools tie tests to your data, and enterprise platforms add governance at a quote-based price. Whichever you pick, the larger decision is whether one winning page for everyone is the right goal for your visitors.

Polish is an AI agent that reads visit signals to rewrite headlines, copy and calls to action for each visitor, then tests variations on live traffic and keeps the ones that convert. If your goal is more signups from visitors who arrive from different sources, see how Polish works.