Roundup
5 Best A/B Testing Tools in 2026
A/B testing tools let you compare page variants and measure which performs better. Here are five options ranging from affordable no-code tools to enterprise experimentation platforms.
1. IterOps
IterOps combines no-code A/B testing with click heatmaps, rage click detection, dead click tracking, and scroll depth measurement. The unique advantage is the feedback loop: use heatmap data to identify where users struggle, create a variant to test a fix, and measure results with per-variant click analytics. Setup is a single JavaScript snippet, and experiments can be launched without code changes.
- Pricing: $9/month with a 7-day free trial
- Best for: Teams that want A/B testing paired with heatmaps at an affordable price
2. Crazy Egg
Crazy Egg includes A/B testing with a visual WYSIWYG editor alongside its heatmap and session recording features. The visual editor makes it easy to change headlines, button text, images, and layout without touching code. Crazy Egg has been in the market since 2006 and has a mature, well-tested editor.
- Pricing: $29+/month
- Best for: CRO teams that want a visual editor for creating test variants
3. VWO (Visual Website Optimizer)
VWO is a dedicated experimentation platform that offers A/B testing, multivariate testing, split URL testing, and personalization. It includes a visual editor, server-side testing capabilities, and statistical analysis with Bayesian and frequentist methods. VWO also includes heatmaps and session recordings in its higher-tier plans.
- Pricing: Custom pricing (starts around $200+/month)
- Best for: Mid-size to enterprise teams that need advanced experimentation features
4. Optimizely
Optimizely is an enterprise experimentation platform used by large organizations. It supports A/B testing, multivariate testing, feature flags, and server-side experiments. Optimizely provides robust statistical analysis and can handle very high traffic volumes. It integrates deeply with analytics platforms, CDPs, and development workflows.
- Pricing: Custom (enterprise)
- Best for: Large organizations with dedicated experimentation teams and high traffic volume
5. Google Optimize (discontinued)
Google Optimize was a free A/B testing tool that integrated tightly with Google Analytics. Google discontinued it in September 2023, leaving a gap in the market for free A/B testing. Former Google Optimize users looking for an affordable replacement may want to consider IterOps ($9/month) or Crazy Egg ($29/month) as the most accessible paid alternatives.
- Pricing: Discontinued
- Status: No longer available as of September 2023
Summary comparison
| Tool | Price | Visual Editor | Heatmaps | Server-side | Status |
|---|---|---|---|---|---|
| IterOps | $9/mo | Yes | Yes | No | Active |
| Crazy Egg | $29+/mo | Yes | Yes | No | Active |
| VWO | $200+/mo | Yes | Yes | Yes | Active |
| Optimizely | Custom | Yes | No | Yes | Active |
| Google Optimize | Free | Yes | No | No | Discontinued |
Frequently asked questions
- What is A/B testing?
- A/B testing (also called split testing) is a method of comparing two versions of a web page or element to see which performs better. Visitors are randomly shown version A or version B, and the tool measures which version achieves a higher conversion rate or engagement.
- What is the cheapest A/B testing tool?
- IterOps is the most affordable dedicated A/B testing tool at $9/month, and it includes click heatmaps alongside testing. Google Optimize was a popular free option but was discontinued in 2023.
- Do I need a developer to run A/B tests?
- Not necessarily. Tools like IterOps and Crazy Egg offer no-code A/B testing that lets you create variants using a visual interface. Enterprise tools like Optimizely and VWO also include visual editors but may require developer involvement for more complex experiments.
- How long should I run an A/B test?
- Most A/B tests should run for at least 1-2 weeks to collect enough data for statistical significance. The exact duration depends on your traffic volume and the size of the expected effect. Ending tests too early can lead to unreliable results.
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