{"site":{"name":"Koji","description":"AI-native customer research platform that helps teams conduct, analyze, and synthesize customer interviews at scale.","url":"https://www.koji.so","contentTypes":["blog","documentation"],"lastUpdated":"2026-10-03T08:46:14.562Z"},"content":[{"type":"blog","id":"c0f8bb98-10da-498c-a35d-5492fbdd70f7","slug":"best-ab-testing-tools-2026","title":"Best A/B Testing Tools in 2026: 10 Platforms Compared (And the Result No Test Can Explain)","url":"https://www.koji.so/blog/best-ab-testing-tools-2026","summary":"A sourced 2026 comparison of 10 A/B testing and experimentation platforms. Optimizely, VWO and AB Tasty publish no price; GrowthBook, Convert Experiences, Statsig and PostHog do. LaunchDarkly publishes a price but meters service connections, so backend architecture can cost more than the whole audience. No experiment explains why a variant lost, which is the gap Koji fills with AI-moderated interviews.","content":"**Short answer:** in 2026 the A/B testing platforms that still publish a self-serve price are **GrowthBook** (free, then $40 per seat per month), **Convert Experiences** (from $299 per month billed annually), **Statsig** ($150/mo for Pro) and **PostHog** (usage-based per event after a free monthly allowance). **Optimizely**, **VWO** and **AB Tasty** all require a sales conversation. **LaunchDarkly** publishes a price but meters something most buyers never check. And every platform on this list answers which variant won while leaving why the loser lost completely untouched, which is where [Koji](https://www.koji.so) comes in.\n\n## The 2026 shortlist at a glance\n\n| Tool | Published price? | What it meters |\n|---|---|---|\n| GrowthBook | Yes, $40 per seat per month | Seats |\n| Convert Experiences | Yes, from $299/month annually | Tested traffic |\n| Statsig | Yes, $150/mo Pro | Events |\n| PostHog | Yes, per event | Events and flag requests |\n| LaunchDarkly | Yes, Foundation | Service connections plus client-side MAU |\n| Mixpanel | No paid figure | Monthly experiment units |\n| Amplitude | No, Custom | Events |\n| Optimizely | No | Not stated |\n| VWO | No | Not stated |\n| AB Tasty | No | Not stated |\n\n## What changed: the classic names went dark\n\nIf you are working from a 2025 shortlist, three of the names on it no longer behave the way they did.\n\n**Optimizely publishes no price for any of its products in 2026.** Its pricing page lists ten product lines and not a single dollar figure. The only guidance offered is that a salesperson will assemble a plan for you. Our full breakdown is in [Optimizely alternatives](/blog/optimizely-alternatives-2026).\n\n**VWO does not publish a price either, and the redirect is the tell.** vwo.com/pricing now returns an HTTP 301 to wingify.com/pricing, where Growth, Pro and Enterprise appear with feature lists and no currency symbols at all. Meanwhile comparison sites print exact VWO prices and label them verified, and those numbers disagree with each other by a factor of four. That spread is the single best argument for not budgeting from an aggregator. See [VWO alternatives](/blog/vwo-alternatives-2026).\n\n**AB Tasty also requires a conversation first**, and if you were shortlisting it specifically as a VWO substitute, read the caveat in that same VWO breakdown before you do.\n\nThis was not three independent decisions. Between January 2025 and May 2026 the experimentation category consolidated hard, and almost every vendor that changed hands also stopped printing a number.\n\n## The platforms that still print a rate\n\n**GrowthBook** is free to start and then $40 per seat per month. Seat-based pricing in a category that mostly meters traffic is unusual, and it is predictable in a way traffic meters are not: your bill moves when you hire, not when you succeed.\n\n**Convert Experiences** starts at $299 per month billed annually. It meters tested traffic, which is the conventional model and the one most buyers already understand.\n\n**Statsig** publishes a full rate card. The Developer tier is free with 2M events per month, unlimited flag and config checks, and 50,000 session replays per month. Pro is $150/mo with 5M events included, then $0.05 per 1K events, and 100,000 session replays per month. Enterprise runs on event-based or experiment-based contracts.\n\n**PostHog** bundles experimentation with analytics and feature flags on one usage-based meter. The free monthly allowance covers 1M analytics events and 1M feature flag requests, after which analytics bills per event from around $0.00005 with the rate falling in published volume bands. One honest caveat: posthog.com/pricing did not render as static text when we fetched it for this article, so that entry rate is the figure multiple independent pricing trackers report rather than a string we read off the vendor page. Confirm your band with PostHog directly.\n\n**Mixpanel and Amplitude** both ship experimentation inside their analytics platforms rather than as standalone products. Neither publishes a paid price. Mixpanel does publish the experiment allowances: the free plan includes 1k MEU per month for experiments and up to 10 active feature flags, and Growth includes 5k MEU and up to 50 active feature flags. Amplitude prints Custom for both Growth and Enterprise.\n\n## The meter nobody checks: LaunchDarkly prices your architecture\n\nLaunchDarkly deserves credit for publishing a price at all in this category, and this article says so plainly. The issue is not secrecy. It is that the Foundation plan bills on two things, and one of them has nothing to do with how many customers you have.\n\nThe first is a service connection, which LaunchDarkly defines on its own pricing page as one server-side SDK connected to one environment for a month. Foundation includes five, and additional connections bill at $10 each per month. The second is client-side audience, billed at $8.33 per 1k client-side MAU per month, where each unique client-side user or device counts as one monthly active user.\n\nRead those together. Twelve backend services deployed across four environments is 12 x 4 = 48 service connections. Subtract the five included and 43 x $10 = $430 per month, before a single end user is counted. To reach that same $430 on the audience meter you would need roughly 51,600 client-side monthly active users, because $430 divided by $8.33 per thousand is 51.6 thousand.\n\nFor a mid-sized B2B product, the line item for how you chose to structure your backend can quietly exceed the line item for everyone who uses the product. The full arithmetic is in [LaunchDarkly alternatives](/blog/launchdarkly-alternatives-2026).\n\n## How to choose\n\n**Match the meter to your growth curve.** Traffic meters punish success. Seat meters punish hiring. Event meters punish instrumentation. Service-connection meters punish microservices. Pick the one least correlated with whatever you expect to grow fastest over the contract term.\n\n**Check whether you can model the bill unaided.** If you cannot, you are buying a relationship rather than a tool. For some enterprise deployments that is correct. It should still be a decision.\n\n**Count the tests you will actually run.** Most teams overbuy. A platform sized for continuous experimentation is wasted on a team shipping one test a month, and the statistical caveats matter more than the tooling at that volume. [A/B testing vs user research](/docs/ab-testing-vs-user-research) covers when a test is the wrong instrument entirely, and [multi-armed bandits vs A/B tests](/docs/multi-armed-bandit-vs-ab-test) covers when adaptive allocation costs you the answer.\n\n## The result no test can explain\n\nAn A/B test is a decision procedure, not an explanation. It tells you that variant B converted 14% better at a given confidence level. It is structurally silent on why.\n\nThat silence is expensive in three specific situations, and every experimentation team recognises all three.\n\n**The losing variant that should have won.** Your hypothesis was good, the execution looked right, and it lost anyway. The test cannot tell you whether the idea was wrong or the implementation was, and those two conclusions lead to opposite next actions.\n\n**The flat result.** No significant difference. The most common outcome in real programmes, and the one that teaches you nothing, because a null result is consistent with the change being irrelevant and with it being invisible.\n\n**The win you cannot generalise.** B won, you ship it, and you still do not know which underlying principle to apply to the next twenty decisions.\n\nAnd some things cannot be tested at all. [Interference between users](/docs/interference-between-users-experiments) explains why your control group may not be a control group, and [quasi-experimental design](/docs/quasi-experimental-design-guide) covers measuring impact when a clean test is not available. Question wording alone can move a number, as [split-ballot experiments](/docs/split-ballot-question-wording-experiments) shows.\n\nThe demand for that missing why is rising sharply. The Condens 2026 report on AI in research analysis, built on a survey of 332 research practitioners and published in May 2026, found the top pain point was that it takes too long to get from data to insights, at 47%, with stakeholders needing faster answers than researchers can provide at 43%. AI helped with part of it and not all: 71% agreed AI lets them analyze data significantly faster, while 71% also agreed that validating AI outputs still takes significant time, and only 21% agreed they can trust AI outputs with minimal review. Maze, across nearly 500 responses, found the share of organizations where research is essential to all levels of business strategy nearly tripled in one year, from 8% in 2025 to 22% in 2026.\n\n## Koji: ask the losing variant why\n\nKoji does not run experiments and does not replace any platform above. It answers the question the experiment structurally cannot.\n\nRun your test. Then point Koji at the people who saw the losing variant, or the ones who converted on neither, and it runs AI-moderated voice or text interviews with them in parallel, on demand, and returns a themed report with quotes. Because the AI interviewer asks every participant the same questions and probes the same way, there is no moderator bias and no interviewer variance across a sample of two hundred.\n\nKoji also covers the quantitative side of the same study. Structured questions come in six types, which are open_ended, scale, single_choice, multiple_choice, ranking and yes_no, so you can put a preference ranking across your three variants and an open probe into the reasoning in one interview and one report. See the [structured questions guide](/docs/structured-questions-guide).\n\nPricing is published and does not scale with your traffic. Koji starts free with 10 credits and no card. Insights is EUR 29 per month with 29 credits, Interviews is EUR 79 per month with 79 credits, and extra credits are a flat EUR 1 each. A text interview costs 1 credit, voice 3, and a report refresh 5. A quality gate means only conversations scoring 3 or better consume a credit. Enterprise adds SSO, isolated infrastructure and a signed DPA.\n\n## Where to go next\n\n- [A/B testing vs user research](/docs/ab-testing-vs-user-research) on when to use each.\n- [Multi-armed bandits vs A/B tests](/docs/multi-armed-bandit-vs-ab-test) on adaptive allocation.\n- [Interference between users](/docs/interference-between-users-experiments) on contaminated control groups.\n- [Quasi-experimental design](/docs/quasi-experimental-design-guide) for when you cannot test.\n- [Concept testing tools](/blog/best-concept-testing-tools-2026) and [message testing tools](/blog/best-message-testing-tools-2026) for pre-launch validation.\n- [Thematic analysis guide](/docs/thematic-analysis-guide) for the coding step Koji automates.\n\n## Explain your last flat test\n\nFind the most recent experiment that returned no significant difference. Run a Koji study on the users who saw it. You will know within hours whether the change was irrelevant or simply invisible, which is the difference between dropping the idea and rebuilding it. [Start free with 10 credits](https://www.koji.so), no card needed.\n\n## Frequently asked questions\n\n### What are the best A/B testing tools in 2026?\n\nGrowthBook and Convert Experiences are the best choices if you want a published self-serve price, Statsig and PostHog are strongest for teams that want experimentation, flags and analytics on one usage-based meter, LaunchDarkly leads on feature management, and Optimizely, VWO and AB Tasty remain the enterprise incumbents but all require a sales conversation. Pair any of them with Koji to learn why a variant won or lost.\n\n### How much do A/B testing tools cost in 2026?\n\nGrowthBook is free to start and then $40 per seat per month. Convert Experiences starts at $299 per month billed annually. Statsig Pro is $150/mo with 5M events included, then $0.05 per 1K events. PostHog bills per event after a free monthly allowance of 1M analytics events and 1M feature flag requests. Optimizely, VWO, AB Tasty and Amplitude publish no paid price at all.\n\n### Why does Optimizely not publish pricing?\n\nOptimizely lists ten product lines on its pricing page and no dollar figure for any of them, directing buyers to a salesperson instead. It is not alone: the experimentation category consolidated heavily between January 2025 and May 2026, and almost every vendor that changed hands also stopped publishing a price. VWO now redirects vwo.com/pricing to wingify.com/pricing, where the tiers appear with no currency symbols.\n\n### What is the catch with LaunchDarkly pricing?\n\nLaunchDarkly publishes its price, but the Foundation plan bills on service connections as well as audience. A service connection is defined as one server-side SDK connected to one environment for a month, five are included, and additional connections cost $10 each per month. Twelve services across four environments is 48 connections, so 43 chargeable connections is $430 per month before any end user is counted, which would require roughly 51,600 client-side MAU to match on the audience meter at $8.33 per 1k.\n\n### Can an A/B test tell you why a variant lost?\n\nNo. A test is a decision procedure that measures the difference between variants at a confidence level, and it is structurally silent on motive. That matters most for flat results, which are the most common outcome and teach you nothing, because a null result is equally consistent with the change being irrelevant and with it being invisible. Koji interviews the people who saw each variant and returns themed reasons with quotes.\n\n### Should I run A/B tests or user interviews?\n\nBoth, in sequence, because they answer different questions. Interviews generate and explain hypotheses; tests decide between them at scale. Running tests without research means burning traffic on guesses, and running research without tests means shipping on anecdote. Koji makes the research half fast enough to sit inside a normal experiment cycle, producing themed interview findings in hours rather than weeks.","category":"Comparisons","lastModified":"2026-10-03T03:35:46.425738+00:00","metaTitle":"Best A/B Testing Tools 2026: 10 Platforms Compared","metaDescription":"GrowthBook, Convert, Statsig and PostHog publish prices. Optimizely, VWO and AB Tasty do not. 10 A/B testing tools compared for 2026.","keywords":["best a/b testing tools","ab testing software 2026","experimentation platforms","optimizely pricing","growthbook pricing","statsig pricing","launchdarkly pricing"],"aiSummary":"A sourced 2026 comparison of 10 A/B testing and experimentation platforms. Optimizely, VWO and AB Tasty publish no price; GrowthBook, Convert Experiences, Statsig and PostHog do. LaunchDarkly publishes a price but meters service connections, so backend architecture can cost more than the whole audience. No experiment explains why a variant lost, which is the gap Koji fills with AI-moderated interviews.","aiKeywords":["a/b testing","experimentation","feature flags","pricing transparency","customer research","AI interviews"],"aiContentType":"comparison","faqItems":[{"answer":"GrowthBook and Convert Experiences are the best choices if you want a published self-serve price, Statsig and PostHog are strongest for teams that want experimentation, flags and analytics on one usage-based meter, LaunchDarkly leads on feature management, and Optimizely, VWO and AB Tasty remain the enterprise incumbents but all require a sales conversation. Pair any of them with Koji to learn why a variant won or lost.","question":"What are the best A/B testing tools in 2026?"},{"answer":"GrowthBook is free to start and then $40 per seat per month. Convert Experiences starts at $299 per month billed annually. Statsig Pro is $150/mo with 5M events included, then $0.05 per 1K events. PostHog bills per event after a free monthly allowance of 1M analytics events and 1M feature flag requests. Optimizely, VWO, AB Tasty and Amplitude publish no paid price at all.","question":"How much do A/B testing tools cost in 2026?"},{"answer":"Optimizely lists ten product lines on its pricing page and no dollar figure for any of them, directing buyers to a salesperson instead. It is not alone: the experimentation category consolidated heavily between January 2025 and May 2026, and almost every vendor that changed hands also stopped publishing a price. VWO now redirects vwo.com/pricing to wingify.com/pricing, where the tiers appear with no currency symbols.","question":"Why does Optimizely not publish pricing?"},{"answer":"LaunchDarkly publishes its price, but the Foundation plan bills on service connections as well as audience. A service connection is defined as one server-side SDK connected to one environment for a month, five are included, and additional connections cost $10 each per month. Twelve services across four environments is 48 connections, so 43 chargeable connections is $430 per month before any end user is counted, which would require roughly 51,600 client-side MAU to match on the audience meter at $8.33 per 1k.","question":"What is the catch with LaunchDarkly pricing?"},{"answer":"No. A test is a decision procedure that measures the difference between variants at a confidence level, and it is structurally silent on motive. That matters most for flat results, which are the most common outcome and teach you nothing, because a null result is equally consistent with the change being irrelevant and with it being invisible. Koji interviews the people who saw each variant and returns themed reasons with quotes.","question":"Can an A/B test tell you why a variant lost?"},{"answer":"Both, in sequence, because they answer different questions. Interviews generate and explain hypotheses; tests decide between them at scale. Running tests without research means burning traffic on guesses, and running research without tests means shipping on anecdote. Koji makes the research half fast enough to sit inside a normal experiment cycle, producing themed interview findings in hours rather than weeks.","question":"Should I run A/B tests or user interviews?"}],"relatedTopics":["a/b testing","experimentation","feature flags","pricing","AI interviews"]}],"pagination":{"total":1,"returned":1,"offset":0}}