Best A/B Testing Tools in 2026: Why I Switched to Stellar

I've been running A/B tests for over a decade. I've used them all—Optimizely, VWO, Google Optimize (RIP), Adobe Target, and a dozen others. Most of them share the same problem: they were built for a different era of the web.

Then I found Stellar, and it changed how I think about experimentation.

Let me explain what's broken with traditional A/B testing tools and why Stellar represents the next generation.

The Problem with Traditional A/B Testing Tools

They're Slow

Most A/B testing tools work by loading a JavaScript snippet that modifies the page after it renders. This causes:

• Flickering: Users see the original version before the variant loads
• Layout shifts: Elements jump around as changes apply
• Performance hits: Additional JavaScript blocks rendering
• Poor Core Web Vitals: Google penalizes slow, janky pages

I've seen A/B testing tools add 200-500ms to page load times. That's not just bad UX—it actively hurts the metrics you're trying to improve.

They're Expensive

Enterprise A/B testing pricing is brutal:

• Optimizely: $50,000-$200,000+/year for enterprise
• VWO: $10,000-$50,000+/year at scale
• Adobe Target: Part of Adobe Experience Cloud (six figures)

Even "affordable" tools often charge $200-500/month for basic functionality. For many businesses, the cost of testing exceeds the value of the optimizations discovered.

They're Complex

Traditional tools require:

• Installing and configuring JavaScript snippets
• Learning proprietary visual editors
• Understanding complex targeting rules
• Managing experiment lifecycles manually
• Interpreting statistical significance correctly

The learning curve is steep enough that many teams hire dedicated CRO specialists just to operate the tools.

They're Built for Marketing, Not Product

Most A/B testing tools were designed for marketing pages—landing pages, homepages, promotional content. They struggle with:

• Single-page applications (React, Vue, Next.js)
• Dynamic content and personalization
• Feature flags and gradual rollouts
• Server-side experiments
• Mobile apps

If you're building a modern web application, traditional tools feel antiquated.

Enter Stellar: A/B Testing for the Modern Web

Stellar takes a fundamentally different approach to experimentation. Instead of bolting testing onto your site as an afterthought, it integrates directly into your development workflow.

What Makes Stellar Different

1. No Flicker, No Performance Hit

Stellar's architecture eliminates the rendering problems that plague traditional tools:

• Tests are resolved before the page renders
• No JavaScript modifying the DOM after load
• Zero impact on Core Web Vitals
• No more apologizing to developers about performance

This isn't a small improvement—it's a fundamental architectural advantage.

2. Developer-First Integration

Stellar was built for modern development workflows:

// Clean, simple API
import { getVariant } from '@stellar/ab';

const variant = await getVariant('pricing-experiment');

if (variant === 'annual-first') {
  // Show annual pricing prominently
} else {
  // Default monthly-first layout
}

No visual editors that break when your site updates. No proprietary scripting languages. Just clean code that integrates with your existing stack.

3. Works with Any Framework

React, Next.js, Vue, Svelte, plain HTML—Stellar works everywhere:

• Server-side rendering support
• Edge function compatibility
• Static site generation
• Client-side SPAs

I've integrated it with Next.js App Router, and it just works. No special configuration, no fighting with hydration issues.

4. Built-in Statistical Rigor

Stellar handles the statistics correctly:

• Bayesian and frequentist analysis options
• Automatic sample size calculations
• Sequential testing support (stop early when results are clear)
• Multi-armed bandit options for optimization
• No more manual significance calculations

The dashboard tells you when you have a winner—and when you don't have enough data yet.

5. Pricing That Makes Sense

This is where Stellar really shines. Their pricing is based on tracked users, not some arbitrary "monthly tracked visitors" metric that enterprise tools use to inflate costs.

For most sites, Stellar costs a fraction of what you'd pay for Optimizely or VWO—often 90% less.

Try Stellar Free →

Real Results with Stellar

Let me share a concrete example. A SaaS client wanted to test their pricing page layout. With their previous tool (VWO), we saw:

• 340ms added page load time
• Visible flicker on mobile
• Developers reluctant to run tests (too much friction)
• $800/month for the "pro" plan

After switching to Stellar:

• Zero performance impact
• No flicker
• Developers integrated tests directly into their sprint workflow
• 75% cost reduction

The test itself? We discovered that showing annual pricing first (with the monthly option secondary) increased annual plan selection by 34%. That insight paid for years of Stellar subscription in the first month.

How Stellar Compares

Stellar vs. Optimizely

Optimizely is powerful but expensive and heavy. For most teams, it's overkill.

Stellar vs. VWO

VWO bundles heatmaps and session recordings, which sounds nice but often goes unused. Stellar focuses on doing A/B testing exceptionally well.

Stellar vs. Google Optimize (Deceased)

Google Optimize was free but limited and has been sunset. If you're looking for a replacement, Stellar is the natural evolution—better performance, better developer experience, and sustainable pricing.

Stellar vs. PostHog/Amplitude Experiments

PostHog and Amplitude are great analytics platforms that added experimentation features. Stellar is an experimentation platform from the ground up. If testing is core to your strategy, Stellar's depth matters.

Setting Up Your First Stellar Experiment

Here's how simple it is:

1. Install the SDK

npm install @stellar/ab

2. Initialize

import { init } from '@stellar/ab';

init({
  projectId: 'your-project-id',
});

3. Create an Experiment

In the Stellar dashboard:

• Name your experiment
• Define variants (control + variations)
• Set traffic allocation
• Define your goal metric

4. Implement in Code

import { getVariant } from '@stellar/ab';

export default async function PricingPage() {
  const variant = await getVariant('pricing-layout-test');

  return (
    <div>
      {variant === 'social-proof' ? (
        <SocialProofPricing />
      ) : (
        <StandardPricing />
      )}
    </div>
  );
}

5. Track Conversions

import { track } from '@stellar/ab';

function handlePurchase() {
  track('purchase', { value: 99 });
  // ... rest of purchase logic
}

That's it. No visual editor wrestling, no flicker, no performance concerns.

Get Started with Stellar →

A/B Testing Best Practices

Regardless of tool, these principles matter:

1. Test One Thing at a Time

Multivariate tests are tempting but require massive traffic. For most sites, sequential A/B tests are more practical.

2. Wait for Statistical Significance

Don't peek and call winners early. Stellar helps by telling you when you have enough data—trust it.

3. Run Tests for Full Weeks

User behavior varies by day of week. Run tests for at least 7-14 days to capture the full pattern.

4. Document Everything

Record your hypothesis, what you tested, and what you learned. Build organizational knowledge.

5. Focus on High-Impact Pages

Test your:

• Pricing page
• Checkout flow
• Key landing pages
• Signup forms

Don't waste experiments on low-traffic pages where you'll never reach significance.

6. Consider the Full Funnel

A change that increases clicks might decrease conversions downstream. Track end-to-end metrics, not just immediate actions.

Common A/B Testing Mistakes

Mistake 1: Stopping tests early "It's at 95% confidence!" — but you've only run for 2 days. Day-of-week effects, traffic anomalies, and random variation can all create false positives. Be patient.

Mistake 2: Testing too many variants Five variants sounds thorough, but you need 5x the traffic to reach significance. Start with A/B, not A/B/C/D/E.

Mistake 3: Ignoring segments Your winning variant might perform great on desktop and terribly on mobile. Stellar lets you analyze by segment—use it.

Mistake 4: Testing without hypothesis "Let's see what happens" isn't a test—it's gambling. Start with a clear hypothesis: "Showing social proof will increase signups because users trust peer validation."

Mistake 5: Never testing at all The biggest mistake is not running experiments. Every assumption about what works is a hypothesis that could be wrong.

The ROI of Proper A/B Testing

Let's do the math:

Scenario: E-commerce site, $100,000/month revenue, 2% conversion rate

With a successful test that improves conversion by 10%:

• New conversion rate: 2.2%
• Additional monthly revenue: $10,000
• Annual impact: $120,000

Cost of Stellar: A fraction of that

One winning test typically pays for years of experimentation tooling. The question isn't whether you can afford to test—it's whether you can afford not to.

Why I Switched to Stellar

After years with enterprise tools, here's what convinced me:

1. Performance matters more than ever. Core Web Vitals affect rankings. Traditional tools hurt performance. Stellar doesn't.

2. Developer experience determines adoption. If developers hate the tool, tests don't get run. Stellar's API is clean and integrates naturally.

3. Enterprise pricing is broken. Paying $50K+/year for experimentation means only large companies can test properly. Stellar democratizes access.

4. Modern architecture wins. Tools built for 2015's web struggle with 2026's frameworks. Stellar was built for modern development.

I still use other tools for specific use cases, but Stellar is my default recommendation for any team getting serious about experimentation.

Start Your Free Trial with Stellar →

Frequently Asked Questions

Is Stellar good for beginners?

Yes. The clean API and focused feature set make it easier to learn than enterprise tools. You can run your first test in under an hour.

Does Stellar work with WordPress/Shopify?

Yes. While Stellar shines with modern frameworks, it works on any website via their JavaScript snippet or server-side integrations.

How does Stellar handle statistical significance?

Stellar offers both frequentist and Bayesian statistical methods. The dashboard clearly indicates when you have a statistically significant winner and when you need more data.

Can I migrate from Optimizely/VWO?

Yes. Most teams can transition their testing program in a few days. Stellar's team can help with migration planning.

What's the catch with Stellar's pricing?

There isn't one. They've built a sustainable business at lower price points by focusing on efficiency and not bundling unnecessary features. You pay for what you use.

Does Stellar offer feature flags?

Yes. Feature flags and A/B testing are closely related, and Stellar supports both. You can use flags for gradual rollouts and testing together.

Final Thoughts

A/B testing is the closest thing to a superpower in digital optimization. It turns opinions into evidence and hunches into insights.

But the tools matter. Slow, expensive, complex tools create friction that kills testing programs. Fast, affordable, developer-friendly tools enable experimentation culture.

Stellar is the tool I wish I'd had a decade ago. If you're serious about conversion optimization—whether you're just starting or frustrated with enterprise tools—give it a try.

Try Stellar Free →