A/B Testing Performance Validation: How to Prove Your Optimisations Actually Work

Your engineering team just spent three months optimizing the checkout flow. New architecture. Faster database queries. Reduced JavaScript bundle size by 40%.

You deploy to production Friday afternoon.

Monday morning, conversion rate has dropped 12%. Support tickets flood in about "weird checkout behavior." Revenue is hemorrhaging.

What happened? The optimization broke a critical feature. But you didn't catch it because you measured performance metrics (page load time) without measuring business metrics (conversion rate, revenue, user satisfaction).

This is why A/B testing isn't just for marketing—it's essential for performance validation. Before rolling out significant changes to all users, you need statistical evidence that improvements actually improve outcomes, not just metrics.

A/B testing for performance means validating that your optimizations deliver better user experiences, faster load times, and improved business results—with measurable confidence before full deployment.

This comprehensive guide teaches you how to design, execute, and analyze performance A/B tests, validate changes with load testing, and make data-driven deployment decisions that improve website performance without breaking user experience.

Why Performance Changes Need A/B Testing

The Performance Paradox

Faster doesn't always mean better—at least not for users and business outcomes.

Examples of "improvements" that backfired:

Change	Performance Metric	Business Metric	Outcome
Lazy-load images	✅ Initial load -30%	❌ Bounce rate +15%	Rolled back
Remove JavaScript framework	✅ Bundle size -60%	❌ Interaction bugs +40%	Rolled back
Aggressive caching	✅ Response time -50%	❌ Stale data complaints +200%	Rolled back
Code splitting	✅ First paint -25%	✅ Conversions +8%	Success ✓

The lesson: Technical metrics (load time, bundle size) don't always correlate with business metrics (conversions, revenue, engagement).

What Can Go Wrong

1. Optimization-Induced Bugs

Performance optimizations introduce complexity:

Race conditions from async loading
Visual glitches from lazy rendering
State management issues from code splitting
Cache invalidation problems

2. Perceptual Performance vs. Measured Performance

Users perceive performance differently than tools measure it:

Measured Load Time: 2.5s
User Perception: "Feels slow"

Why?
- Content shifts after initial render (CLS)
- Buttons don't respond immediately (FID)
- Skeleton screens feel empty

3. Device and Network Variation

Optimization that helps high-end devices might hurt low-end:

Heavy JavaScript parsing on slow CPUs
Memory pressure from caching strategies
Network waterfall issues on 3G

The Cost of Failed Deployments

Company Size	Average Revenue/Hour	1-Hour Outage Cost	1-Day Degraded Performance Cost
Startup (Series A)	$500	$500	$4,000
Mid-size (Series B)	$5,000	$5,000	$40,000
Enterprise	$50,000	$50,000	$400,000

A/B testing de-risks deployments by validating changes on a subset before committing fully.

The Performance A/B Testing Framework

Core Principles

1. Define Success Before Testing

❌ Vague goal: "Make the site faster"

✅ Clear hypothesis:

What: Reduce checkout page load time by implementing progressive image loading
Why: Hypothesis: Faster page loads will reduce cart abandonment
Success criteria:
- Technical: LCP reduced from 3.2s to 2.0s
- Business: Cart abandonment reduced by 10% (from 65% to 58.5%)
- Risk: No increase in image loading errors >0.5%

2. Test Multiple Dimensions

Dimension	Metrics to Track
Performance	LCP, FID, CLS, TTFB, Page Load Time
Business	Conversion rate, revenue per visitor, session duration
Reliability	Error rate, crash rate, timeout rate
User Satisfaction	Bounce rate, pages per session, return rate

3. Statistical Rigor

Don't trust hunches. Use proper statistical analysis:

// A/B test significance calculation
function calculateSignificance(controlGroup, treatmentGroup) {
  const controlSuccess = controlGroup.conversions;
  const controlTotal = controlGroup.visitors;
  const treatmentSuccess = treatmentGroup.conversions;
  const treatmentTotal = treatmentGroup.visitors;

  const controlRate = controlSuccess / controlTotal;
  const treatmentRate = treatmentSuccess / treatmentTotal;

  const pooledRate = (controlSuccess + treatmentSuccess) / (controlTotal + treatmentTotal);

  // Standard error
  const se = Math.sqrt(pooledRate * (1 - pooledRate) * (1 / controlTotal + 1 / treatmentTotal));

  // Z-score
  const z = (treatmentRate - controlRate) / se;

  // P-value (two-tailed)
  const pValue = 2 * (1 - normalCDF(Math.abs(z)));

  return {
    controlRate: (controlRate * 100).toFixed(2) + '%',
    treatmentRate: (treatmentRate * 100).toFixed(2) + '%',
    improvement: (((treatmentRate - controlRate) / controlRate) * 100).toFixed(2) + '%',
    zScore: z.toFixed(2),
    pValue: pValue.toFixed(4),
    significant: pValue < 0.05,
    confidence: ((1 - pValue) * 100).toFixed(1) + '%',
  };
}

// Example usage
const results = calculateSignificance(
  { conversions: 150, visitors: 2500 }, // Control: 6% conversion
  { conversions: 175, visitors: 2500 }, // Treatment: 7% conversion
);

console.log(results);
// {
//   controlRate: '6.00%',
//   treatmentRate: '7.00%',
//   improvement: '16.67%',
//   zScore: '2.15',
//   pValue: '0.0316',
//   significant: true,
//   confidence: '96.8%'
// }

Statistical Requirements:

Minimum sample size: 1000 visitors per variant
Statistical significance: p-value < 0.05 (95% confidence)
Test duration: At least 7 days (capture weekly patterns)
Avoid peeking: Don't stop tests early based on intermediate results

Setting Up A/B Tests for Performance

Architecture Options:

1. Client-Side A/B Testing

// pages/_app.js - Next.js example
import { useEffect } from 'react';
import { assignUserToVariant, trackEvent } from '@/lib/analytics';

export default function App({ Component, pageProps }) {
  useEffect(() => {
    // Assign user to A/B test variant
    const variant = assignUserToVariant('perf-optimization-2026-02');

    // Track performance metrics
    if ('web-vitals' in window) {
      onCLS((metric) => trackEvent('web-vital', { ...metric, variant }));
      onFID((metric) => trackEvent('web-vital', { ...metric, variant }));
      onLCP((metric) => trackEvent('web-vital', { ...metric, variant }));
    }

    // Track business metrics
    trackEvent('page_view', { variant });
  }, []);

  return <Component {...pageProps} />;
}

// lib/analytics.js
export function assignUserToVariant(experimentId) {
  const stored = localStorage.getItem(`experiment_${experimentId}`);

  if (stored) return stored;

  // Consistent hashing based on user ID or session
  const userId = getCurrentUserId() || getSessionId();
  const hash = simpleHash(userId + experimentId);
  const variant = hash % 2 === 0 ? 'control' : 'treatment';

  localStorage.setItem(`experiment_${experimentId}`, variant);

  return variant;
}

export function trackEvent(eventName, properties) {
  // Send to analytics platform
  analytics.track(eventName, {
    timestamp: Date.now(),
    url: window.location.href,
    ...properties,
  });
}

2. Server-Side A/B Testing

// middleware/ab-test.js
export function abTestMiddleware(req, res, next) {
  const experimentId = 'perf-optimization-2026-02';

  // Check for existing assignment
  let variant = req.cookies[`exp_${experimentId}`];

  if (!variant) {
    // Assign variant (50/50 split)
    const userId = req.session?.userId || req.sessionID;
    const hash = crypto
      .createHash('md5')
      .update(userId + experimentId)
      .digest('hex');
    variant = parseInt(hash, 16) % 2 === 0 ? 'control' : 'treatment';

    // Store in cookie (30 days)
    res.cookie(`exp_${experimentId}`, variant, {
      maxAge: 30 * 24 * 60 * 60 * 1000,
      httpOnly: true,
      sameSite: 'lax',
    });
  }

  req.experiment = {
    id: experimentId,
    variant: variant,
  };

  next();
}

// Apply different optimization strategies
app.get('/checkout', abTestMiddleware, (req, res) => {
  const variant = req.experiment.variant;

  if (variant === 'treatment') {
    // Optimized version: preload critical assets, lazy load below fold
    res.render('checkout-optimized', {
      preloadImages: true,
      lazyLoadThreshold: 800,
      enablePrefetch: true,
    });
  } else {
    // Control version: current implementation
    res.render('checkout', {});
  }
});

3. Edge/CDN-Level A/B Testing

// Cloudflare Workers example
addEventListener('fetch', (event) => {
  event.respondWith(handleRequest(event.request));
});

async function handleRequest(request) {
  const url = new URL(request.url);
  const experimentId = 'perf-optimization-2026-02';

  // Get or assign variant
  const cookie = request.headers.get('Cookie') || '';
  const variantMatch = cookie.match(new RegExp(`exp_${experimentId}=([^;]+)`));
  let variant = variantMatch ? variantMatch[1] : null;

  if (!variant) {
    // Assign based on request ID
    const requestId = request.headers.get('cf-ray') || crypto.randomUUID();
    const hash = await crypto.subtle.digest('SHA-256', new TextEncoder().encode(requestId));
    variant = new Uint8Array(hash)[0] % 2 === 0 ? 'control' : 'treatment';
  }

  // Route to appropriate origin
  const origin = variant === 'treatment' ? 'https://optimized.example.com' : 'https://app.example.com';

  const modifiedRequest = new Request(origin + url.pathname, request);
  const response = await fetch(modifiedRequest);

  // Set variant cookie
  const newResponse = new Response(response.body, response);
  newResponse.headers.set('Set-Cookie', `exp_${experimentId}=${variant}; Max-Age=2592000; Path=/; SameSite=Lax`);
  newResponse.headers.set('X-Experiment-Variant', variant);

  return newResponse;
}

Comprehensive Performance Metrics

Technical Performance Metrics

Core Web Vitals (Google's user-focused metrics):

Metric	Good	Needs Improvement	Poor
LCP (Largest Contentful Paint)	≤ 2.5s	2.5s - 4.0s	> 4.0s
FID (First Input Delay)	≤ 100ms	100ms - 300ms	> 300ms
CLS (Cumulative Layout Shift)	≤ 0.1	0.1 - 0.25	> 0.25

// Comprehensive performance tracking
import { onCLS, onFID, onLCP, onFCP, onTTFB } from 'web-vitals';

function trackWebVitals(variant) {
  const metrics = {};

  onLCP((metric) => {
    metrics.lcp = metric.value;
    sendMetric('lcp', metric.value, variant);
  });

  onFID((metric) => {
    metrics.fid = metric.value;
    sendMetric('fid', metric.value, variant);
  });

  onCLS((metric) => {
    metrics.cls = metric.value;
    sendMetric('cls', metric.value, variant);
  });

  onFCP((metric) => {
    metrics.fcp = metric.value;
    sendMetric('fcp', metric.value, variant);
  });

  onTTFB((metric) => {
    metrics.ttfb = metric.value;
    sendMetric('ttfb', metric.value, variant);
  });

  // Additional custom metrics
  window.addEventListener('load', () => {
    const perfData = performance.getEntriesByType('navigation')[0];

    sendMetric('dom_content_loaded', perfData.domContentLoadedEventEnd - perfData.domContentLoadedEventStart, variant);
    sendMetric('page_load_time', perfData.loadEventEnd - perfData.fetchStart, variant);

    // Resource timing
    const resources = performance.getEntriesByType('resource');
    const jsSize = resources.filter((r) => r.name.endsWith('.js')).reduce((sum, r) => sum + (r.transferSize || 0), 0);
    const cssSize = resources.filter((r) => r.name.endsWith('.css')).reduce((sum, r) => sum + (r.transferSize || 0), 0);
    const imgSize = resources
      .filter((r) => r.initiatorType === 'img')
      .reduce((sum, r) => sum + (r.transferSize || 0), 0);

    sendMetric('js_bundle_size', jsSize, variant);
    sendMetric('css_bundle_size', cssSize, variant);
    sendMetric('image_total_size', imgSize, variant);
  });
}

function sendMetric(name, value, variant) {
  navigator.sendBeacon(
    '/api/metrics',
    JSON.stringify({
      metric: name,
      value: value,
      variant: variant,
      timestamp: Date.now(),
      url: window.location.pathname,
      userAgent: navigator.userAgent,
      connection: navigator.connection?.effectiveType || 'unknown',
    }),
  );
}

Business Impact Metrics

Conversion Funnel Tracking:

// Track conversion funnel for both variants
class ConversionFunnel {
  constructor(variant) {
    this.variant = variant;
    this.events = [];
  }

  trackStep(stepName, metadata = {}) {
    const event = {
      step: stepName,
      timestamp: Date.now(),
      variant: this.variant,
      ...metadata,
    };

    this.events.push(event);

    // Send to analytics
    analytics.track('funnel_step', event);
  }

  trackConversion(revenue = 0) {
    analytics.track('conversion', {
      variant: this.variant,
      revenue: revenue,
      steps: this.events.length,
      duration: Date.now() - this.events[0].timestamp,
    });
  }
}

// Usage
const funnel = new ConversionFunnel(variant);

// Checkout flow
funnel.trackStep('view_cart', { itemCount: cart.items.length, cartValue: cart.total });
funnel.trackStep('begin_checkout');
funnel.trackStep('add_payment_info');
funnel.trackStep('add_shipping_info');
funnel.trackStep('review_order');
funnel.trackConversion(order.total);

Comparative Analysis Dashboard

// Generate A/B test results dashboard
async function generateABTestReport(experimentId, startDate, endDate) {
  const controlData = await getMetrics(experimentId, 'control', startDate, endDate);
  const treatmentData = await getMetrics(experimentId, 'treatment', startDate, endDate);

  return {
    summary: {
      experimentId,
      startDate,
      endDate,
      controlVisitors: controlData.visitors,
      treatmentVisitors: treatmentData.visitors,
    },

    performance: {
      lcp: {
        control: calculatePercentile(controlData.lcp, 75),
        treatment: calculatePercentile(treatmentData.lcp, 75),
        improvement: calculateImprovement(controlData.lcp, treatmentData.lcp),
        significant: calculateSignificance(controlData.lcp, treatmentData.lcp),
      },
      fid: {
        control: calculatePercentile(controlData.fid, 75),
        treatment: calculatePercentile(treatmentData.fid, 75),
        improvement: calculateImprovement(controlData.fid, treatmentData.fid),
        significant: calculateSignificance(controlData.fid, treatmentData.fid),
      },
      cls: {
        control: calculatePercentile(controlData.cls, 75),
        treatment: calculatePercentile(treatmentData.cls, 75),
        improvement: calculateImprovement(controlData.cls, treatmentData.cls),
        significant: calculateSignificance(controlData.cls, treatmentData.cls),
      },
    },

    business: {
      conversionRate: {
        control: controlData.conversions / controlData.visitors,
        treatment: treatmentData.conversions / treatmentData.visitors,
        improvement:
          (treatmentData.conversions / treatmentData.visitors - controlData.conversions / controlData.visitors) /
          (controlData.conversions / controlData.visitors),
        significant: calculateSignificance(
          { conversions: controlData.conversions, visitors: controlData.visitors },
          { conversions: treatmentData.conversions, visitors: treatmentData.visitors },
        ),
      },
      revenuePerVisitor: {
        control: controlData.revenue / controlData.visitors,
        treatment: treatmentData.revenue / treatmentData.visitors,
        improvement:
          (treatmentData.revenue / treatmentData.visitors - controlData.revenue / controlData.visitors) /
          (controlData.revenue / controlData.visitors),
      },
      bounceRate: {
        control: controlData.bounces / controlData.visitors,
        treatment: treatmentData.bounces / treatmentData.visitors,
        improvement:
          (treatmentData.bounces / treatmentData.visitors - controlData.bounces / controlData.visitors) /
          (controlData.bounces / controlData.visitors),
      },
    },

    reliability: {
      errorRate: {
        control: controlData.errors / controlData.requests,
        treatment: treatmentData.errors / treatmentData.requests,
        improvement:
          (treatmentData.errors / treatmentData.requests - controlData.errors / controlData.requests) /
          (controlData.errors / controlData.requests),
      },
    },

    recommendation: generateRecommendation(controlData, treatmentData),
  };
}

function generateRecommendation(control, treatment) {
  const performanceImproved = treatment.avgLCP < control.avgLCP * 0.9; // 10% improvement
  const conversionImproved =
    treatment.conversions / treatment.visitors > (control.conversions / control.visitors) * 1.05; // 5% improvement
  const errorsAcceptable = treatment.errors / treatment.requests <= (control.errors / control.requests) * 1.1; // No more than 10% error increase

  if (performanceImproved && conversionImproved && errorsAcceptable) {
    return {
      decision: 'SHIP',
      confidence: 'high',
      reason: 'Treatment shows significant performance and business improvements without reliability degradation',
    };
  } else if (performanceImproved && errorsAcceptable && !conversionImproved) {
    return {
      decision: 'INVESTIGATE',
      confidence: 'medium',
      reason: 'Performance improved but no business impact. Investigate user experience or tracking issues.',
    };
  } else if (!errorsAcceptable) {
    return {
      decision: 'DO_NOT_SHIP',
      confidence: 'high',
      reason: 'Treatment introduces reliability issues. Fix errors before considering deployment.',
    };
  } else {
    return {
      decision: 'CONTINUE_TESTING',
      confidence: 'low',
      reason: 'Insufficient data or mixed signals. Continue test for more confidence.',
    };
  }
}

Load Testing for Performance Validation

Why Load Testing Matters

A/B tests validate user experience. But what happens when traffic spikes 10x? Does your optimization hold up under load?

Load testing validates:

Performance under concurrent users
Resource utilization (CPU, memory, database connections)
Bottlenecks and breaking points
Cache effectiveness
Auto-scaling behavior

Load Testing Strategy

// k6 load test script
import http from 'k6/http';
import { check, sleep } from 'k6';
import { Rate } from 'k6/metrics';

export let errorRate = new Rate('errors');

export let options = {
  stages: [
    { duration: '2m', target: 100 }, // Ramp up to 100 users
    { duration: '5m', target: 100 }, // Stay at 100 users
    { duration: '2m', target: 200 }, // Ramp to 200 users
    { duration: '5m', target: 200 }, // Stay at 200 users
    { duration: '2m', target: 0 }, // Ramp down to 0
  ],
  thresholds: {
    http_req_duration: ['p(95)<500', 'p(99)<1000'], // 95% < 500ms, 99% < 1s
    errors: ['rate<0.01'], // Error rate < 1%
  },
};

export default function () {
  // Test checkout flow (optimized version)
  let response = http.get('https://staging.example.com/checkout', {
    headers: { 'X-Experiment-Variant': 'treatment' },
  });

  check(response, {
    'status is 200': (r) => r.status === 200,
    'page load < 2s': (r) => r.timings.duration < 2000,
    'LCP < 2.5s': (r) => {
      // Parse Server-Timing header for LCP
      const serverTiming = r.headers['Server-Timing'];
      if (serverTiming) {
        const lcpMatch = serverTiming.match(/lcp;dur=([0-9.]+)/);
        return lcpMatch && parseFloat(lcpMatch[1]) < 2500;
      }
      return false;
    },
  }) || errorRate.add(1);

  sleep(1);

  // Submit checkout
  response = http.post(
    'https://staging.example.com/api/checkout',
    JSON.stringify({
      paymentMethod: 'card',
      shippingAddress: {
        /* ... */
      },
    }),
    {
      headers: {
        'Content-Type': 'application/json',
        'X-Experiment-Variant': 'treatment',
      },
    },
  );

  check(response, {
    'checkout succeeds': (r) => r.status === 200,
    'response time < 1s': (r) => r.timings.duration < 1000,
  }) || errorRate.add(1);

  sleep(2);
}

Comparing Load Test Results

Metric	Control (Baseline)	Treatment (Optimized)	Change
p95 Response Time	850ms	450ms	✅ -47%
p99 Response Time	1200ms	800ms	✅ -33%
Requests/sec	450	520	✅ +16%
Error Rate	0.5%	0.4%	✅ -20%
CPU Utilization	65%	55%	✅ -15%
Memory Usage	2.1 GB	2.3 GB	⚠️ +10%
Database Queries/sec	1200	980	✅ -18%

Verdict: Treatment variant performs significantly better under load with acceptable memory trade-off.

Progressive Rollout Strategy

Canary Deployment with Monitoring

Don't roll out to 50% of users immediately. Use progressive rollout:

Phase 1: Internal Testing (1-2 days)

100% of employee traffic
Validate functionality
Monitor error rates

Phase 2: Canary (2-3 days)

5% of production traffic
High-priority monitoring
Quick rollback capability

Phase 3: Gradual Rollout (7-14 days)

Day 1-3: 10% traffic
Day 4-7: 25% traffic
Day 8-10: 50% traffic
Day 11-14: 100% traffic

Phase 4: Monitoring Period (30 days)

Watch for delayed effects
Monitor long-term metrics
Gather user feedback

// Progressive rollout configuration
const rolloutSchedule = [
  { date: '2026-03-01', percentage: 5, stage: 'canary' },
  { date: '2026-03-03', percentage: 10, stage: 'gradual' },
  { date: '2026-03-05', percentage: 25, stage: 'gradual' },
  { date: '2026-03-08', percentage: 50, stage: 'gradual' },
  { date: '2026-03-12', percentage: 100, stage: 'full' },
];

function getUserRolloutPercentage(userId, experimentId) {
  const currentDate = new Date();
  const activeRollout = rolloutSchedule.find((r) => new Date(r.date) <= currentDate);

  if (!activeRollout) return 0;

  // Consistent hashing to determine if user is in rollout
  const hash = simpleHash(userId + experimentId);
  const userPercentile = (hash % 100) + 1;

  return userPercentile <= activeRollout.percentage ? 'treatment' : 'control';
}

Analyzing Results and Making Decisions

The Decision Matrix

Performance	Business Impact	Errors	Decision
✅ Improved	✅ Improved	✅ No increase	SHIP – Full rollout
✅ Improved	➖ Neutral	✅ No increase	SHIP – Worthwhile optimization
✅ Improved	❌ Negative	✅ No increase	INVESTIGATE – UX issue?
✅ Improved	➖ Neutral	❌ Increased	FIX – Address errors first
➖ Neutral	✅ Improved	✅ No increase	SHIP – Business wins
❌ Regressed	*	*	DO NOT SHIP – Fix regression
*	*	❌ Significant increase	DO NOT SHIP – Fix reliability

Real-World Example: Checkout Optimization

Hypothesis: Lazy-loading payment provider scripts will reduce LCP and improve conversion rate.

Implementation: Load Stripe.js only when user clicks "Add Payment Method" instead of on page load.

Results after 14 days (10,000 visitors per variant):

Performance Metrics:

LCP (75th percentile):
  Control: 3.1s
  Treatment: 2.3s
  Improvement: -26% ✅
  Significant: Yes (p < 0.001)

Page Load Time:
  Control: 2.8s
  Treatment: 2.1s
  Improvement: -25% ✅
  Significant: Yes (p < 0.001)

JavaScript Bundle Size:
  Control: 1.2 MB
  Treatment: 850 KB (initial), 1.2 MB (after interaction)
  Improvement: -29% initial ✅

Business Metrics:

Conversion Rate:
  Control: 4.2% (420 / 10,000)
  Treatment: 3.9% (390 / 10,000)
  Change: -7.1% ❌
  Significant: Yes (p = 0.042)

Revenue Per Visitor:
  Control: $3.25
  Treatment: $3.01
  Change: -7.4% ❌

Cart Abandonment at Payment Step:
  Control: 35%
  Treatment: 41%
  Change: +17% ❌

Error Metrics:

Payment Script Load Errors:
  Control: 0.1%
  Treatment: 2.3% ❌

Timeout Errors:
  Control: 0.3%
  Treatment: 1.1% ❌

Analysis: The optimization improved technical performance metrics significantly, but negatively impacted business metrics and reliability. Investigation revealed:

Race condition: Users clicking "Add Payment Method" before script loaded saw error
UX confusion: Delay between click and payment form appearing caused abandonment
Mobile impact: Slower devices experienced more script loading failures

Decision: DO NOT SHIP. Optimization improved performance but hurt revenue and reliability. Refined approach needed.

Refined Approach:

Preload script on mouseover/focus of payment section (compromise)
Add loading indicator during script fetch
Implement robust error handling and retry logic

Connecting Performance to Quality

Performance validation doesn't exist in isolation—it's part of your comprehensive quality strategy. Combine A/B testing with continuous testing in CI/CD to catch performance regressions before production. Use E2E testing to validate that optimizations don't break critical user journeys. Monitor production continuously with web application monitoring.

Validate Changes Confidently

You now understand how to use A/B testing for rigorous performance validation, combining technical metrics with business impact and reliability signals. You know how to design experiments, collect data, analyze results statistically, and make confident deployment decisions.

The difference between shipping optimizations that help versus hurt is measuring what actually matters.

Comprehensive Performance Testing with ScanlyApp

ScanlyApp provides automated performance validation for every deployment:

✅ Performance Regression Detection – Automatically compare response times across deploys
✅ Real User Monitoring – Track actual user experiences in production
✅ Synthetic A/B Testing – Validate changes across multiple endpoints
✅ Core Web Vitals Tracking – Monitor LCP, FID, CLS continuously
✅ Load Testing Integration – Simulate user traffic before deployment
✅ Automated Rollback Triggers – Stop bad deployments automatically

Start Validating Performance →

Make evidence-based deployment decisions with comprehensive website performance testing.

Questions about implementing A/B testing or performance validation for your specific use case? Talk to our performance experts—we'll help you build a rigorous testing strategy.