Test versions of a page, headline, or layout against each other to see which one converts better.
A/B testing software spans two very different environments: browser-based conversion experiments on marketing pages and feature-flag experiments inside software products. Some tools offer visual editors for marketers, while others require engineering integration but can test server-side behavior, product features or warehouse-defined metrics. Statistical methods, targeting controls and how experiments interact with feature releases are key dividing lines.
By SEM Solutions Editorial Team. Researched from vendor documentation, not hands-on testing. Ordered by Overall Score. Checked October 7, 2026. How we research
For product analytics experiments
#1 of 8 in A/B Testing
PostHog integrates experiments with feature flags and product analytics, so teams can define a treatment, expose users through a flag and evaluate behavior using product metrics in the same environment. Session replay can provide additional context when quantitative results need investigation. The workflow suits software teams that already instrument product events and want experimentation tied closely to that behavioral data.
Controlled feature releases
#2 of 8 in A/B Testing
LaunchDarkly combines feature flags, progressive rollouts and experimentation so engineering teams can separate deploying code from exposing a feature to users. Experiments can operate on flagged product experiences while release targeting and kill switches provide operational control around the same change.
For warehouse-native experiments
#3 of 8 in A/B Testing
Statsig combines feature gates, experimentation and product analytics, including a warehouse-native mode that can run experiment analysis against data in the company's own warehouse. Its metric tooling supports more specialized experiment designs and variance-reduction approaches. It is a good match for data-mature product organizations that want experimentation tied to established warehouse metrics instead of a separate analytics silo.
Landing-page traffic tests
#4 of 8 in A/B Testing
Unbounce is focused on landing-page conversion work. Marketers can create multiple page variants and either run conventional tests or use Smart Traffic to route visitors toward the variant predicted to fit them after an initial learning period.
Feature experimentation
#5 of 8 in A/B Testing
Optimizely's feature-management and experimentation tooling connects feature flags with controlled rollouts and experiments. Consider it when experimentation is part of software delivery and the treatment may live in application code instead of a visual page editor; product and engineering teams can expose different experiences to selected audiences, measure outcomes and adjust release behavior without treating every test as a webpage variation.
Behavior-informed page tests
#7 of 8 in A/B Testing
Crazy Egg combines A/B testing with visual behavior tools such as heatmaps and recordings, allowing marketers to use observed page interactions when deciding what to test. Experiments can compare page variants while the surrounding behavior data helps explain where visitors click or stop engaging.
For web experimentation programs
#8 of 8 in A/B Testing
Convert supports A/B, split-URL, multivariate and multipage experiments, with visual and code editing options plus audience targeting. Its experimentation controls also include QA, collision handling and statistical settings, making it suitable for teams running an ongoing website optimization program beyond occasional headline tests; full-stack capabilities extend the platform beyond changes that can be made only in the browser.
Ranked by Overall Score: our Editor Rating and Market Signal, 50/50. Market Signal uses Ahrefs and Semrush data through September 2026, pulled October 6 to 12, 2026. How we score
Product engineering suite combining analytics, replay and feature flags.
Best for: Product and engineering teams that want analytics, experimentation, feature delivery, replay, and data tooling in a developer-oriented stack.
Runtime control platform for feature flags, experimentation and progressive software releases.
Best for: Engineering and product teams that need controlled feature releases, experimentation and operational kill switches.
Product development platform combining experimentation, feature gates, analytics and session replay.
Best for: Product and engineering teams that want feature delivery and statistically measured experiments on shared data.
Landing page builder with A/B testing, AI-driven Smart Traffic routing and an MCP server.
Best for: Paid acquisition teams and agencies that build many campaign landing pages and want automated variant routing to lift conversions.
Digital experience platform combining web and feature experimentation with CMS, personalization and commerce.
Best for: Enterprises running a structured testing program across marketing sites and product code, with engineers available for SDK work.
Experimentation and personalization platform formed by the merger of VWO and AB Tasty.
Best for: Teams running a structured, ongoing A/B testing program.
Website behavior analytics combining heatmaps, recordings and surveys.
Best for: Marketing and conversion teams that want visual website behavior evidence plus lightweight testing in the same tool.
Privacy-conscious experimentation platform for A/B, split-URL, multipage, and personalization testing.
Best for: Optimization teams and agencies that need controlled website experimentation with detailed targeting and integrations into existing analytics stacks.
Client-side tests usually modify or swap webpage elements in the visitor's browser, which makes them accessible to marketing teams and visual editors. Server-side or code-level experiments decide the treatment inside the application or backend before the interface is rendered. That approach can test deeper product behavior but normally requires engineering implementation and reliable event instrumentation.
There is no universal number of days. A test needs enough observations for the planned statistical method and should usually cover normal variations in traffic and user behavior, so do not stop it as soon as a favorable result appears. Teams should define the primary metric, expected effect and stopping rule before launch so decisions are not made opportunistically.
Yes, PostHog, LaunchDarkly and Statsig all offer a free plan. Free tiers usually come with usage limits, so check whether they cover your volume before relying on them long-term.
Among the tools listed here whose main product is in this category and whose pricing we've verified, Unbounce has the lowest listed starting price: $22/mo (annual billing). Starting price isn't the whole picture, so check what's included at that tier before deciding.