Most teams know Amplitude as their analytics engine. But fewer realize that Amplitude also offers two distinct A/B testing capabilities - Web Experiment and Feature Experiment - each designed for very different use cases.

If you choose the wrong one, you risk slow insights, inconsistent data, or experiments that simply don’t ship.

As analytics and experimentation practitioners, we see this confusion across growth and product teams all the time. So in this guide, we break down Amplitude’s two A/B testing methods, when to use each, and how to integrate them into a scalable experimentation workflow.

Whether you’re optimizing a landing page, running in-product tests, or building a full experimentation program, this article will help you understand which approach unlocks the most value for your team.

What Is an Amplitude Web Experiment?

Amplitude Web Experiment is built for marketing websites, landing pages, and browser-based experiences.
You can think of it as Amplitude’s answer to tools like Google Optimize (RIP), VWO, or Optimizely Web  but tightly integrated with your analytics.

What it’s best for

• Landing page A/B tests
  
  
• Headline, CTA, layout variations
  
  
• Marketing site conversion optimization
  
  
• Funnel drop-off experiments
  
  
• Rapid testing without engineering support
  
  

How it works

Web Experiment uses client-side rendering to change text, images, layouts, or UI elements directly in the browser. This makes it ideal for fast marketing tests or CRO experiments.

Key strengths

• No-code visual editor
  
  
• Fast iteration for marketing teams
  
  
• Tight integration with Amplitude Analytics
  
  
• Perfect for traffic-based experiments
  
  
• Great for acquisition and conversion insights
  
  

Limitations

• Not ideal for product feature rollouts
  
  
• Client-side changes can affect page load
  
  
• Less control and precision than server-side tests
  
  
• Not suited for deep product logic or backend changes
  
  

What Is Amplitude Feature Experiment?

Amplitude Feature Experiment is the platform's server-side feature flagging and experimentation system.
It’s built for product teams shipping features, managing rollouts, and validating in-product behavior.

Feature Experiment competes with LaunchDarkly, GrowthBook, and Statsig but with a native analytics layer on top.

What it’s best for

• Feature rollouts and staged releases
  
  
• Testing product flows, onboarding, or settings
  
  
• Pricing experiments → inside the product
  
  
• Personalization models
  
  
• Backend logic experiments
  
  
• Long-term experiments with precise targeting
  
  

How it works

Feature flags are controlled at the server or SDK level. This gives teams full control, removes flicker, and ensures the experiment ties directly to product behavior captured in Amplitude Analytics.

Key strengths

• High accuracy and performance
  
  
• Deep control over experiment logic
  
  
• Works across web, mobile, backend
  
  
• Perfect for product + engineering workflows
  
  
• Supports progressive rollouts and kill-switch flags
  
  
• No dependency on page load times
  
  

Limitations

• Requires engineering implementation
  
  
• Not ideal for quick marketing experiments
  
  
• Slower iteration if engineering bandwidth is tight
  

‍

How to Choose: When to Use Each

Use Web Experiment when:

• You want fast insights
  
  
• You’re optimizing acquisition or conversion
  
  
• You need to test copy, layout, or UI without code
  
  
• Your experiments don’t affect business logic
  
  

Example: Testing three landing page headlines to increase trial signups.

‍

Use Feature Experiment when:

• You're releasing or validating new features
  
  
• You need server-side control or backend logic
  
  
• You want precise targeting and no flicker
  
  
• You’re building a long-term experimentation program
  
  

Example: Rolling out a new onboarding flow to 20% of users before full release.

Why This Matters for Growth Teams

Choosing between Web Experiment and Feature Experiment isn’t just tactical — it shapes your entire experimentation velocity.

The right setup helps your teams:

• Run more experiments with higher accuracy
  
  
• Unify data into one analytics system
  
  
• Reduce engineering bottlenecks
  
  
• Make experimentation part of daily workflows
  
  
• Ship and validate features faster
  
  
• Improve conversion and retention with less guesswork
  
  

At Adasight, we see the best growth teams combine both:

Web Experiment → to scale fast marketing tests
Feature Experiment → to validate product decisions with high precision

Together, they create a continuous learning loop across the entire customer journey.

How Adasight Helps Teams Implement Amplitude Experimentation

Most teams underestimate what’s needed for a reliable experimentation engine:
tracking, governance, data quality, naming structure, experiment design, and post-analysis.

Our fractional experimentation team helps you:

• Implement Amplitude Experiment properly (web + feature)
  
  
• Build scalable tracking for experiment analysis
  
  
• Create experiment frameworks & naming standards
  
  
• Train product, growth, and marketing teams
  
  
• Build AI agents for experiment reporting
  
  
• Run experimentation "loops" across your funnels
  
  

Whether you’re just adopting Amplitude or scaling experimentation across your org, we help you move faster with fewer mistakes.

Conclusion

Amplitude Web Experiment and Feature Experiment solve two very different problems, and the magic happens when teams use the right one at the right time.

If you want to scale testing velocity, improve decision-making, and build a real experimentation culture, you need a unified system across marketing and product.

‍