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Test a React app’s performance in two complementary ways: run repeatable lab checks on a production-like build before release, then measure real-user experience in the field. Lighthouse can help catch regressions, but it cannot replace field data—and TTFB is a supporting metric, not a Core Web Vital.
Which metrics should you test?
Core Web Vitals are Largest Contentful Paint (LCP), Interaction to Next Paint (INP), and Cumulative Layout Shift (CLS). Google’s “good” thresholds are LCP at or below 2.5 seconds, INP at or below 200 milliseconds, and CLS at or below 0.1. Evaluate the 75th percentile separately for mobile and desktop; a good score on one developer’s device does not establish that most users have a good experience. Google’s Web Vitals guidance explains the metrics and thresholds.
Time to First Byte (TTFB) measures the time from navigation start until the first response byte begins arriving. It is useful context because it comes before later loading metrics, but it is not a Core Web Vital. Google gives a rough target of 0.8 seconds or less, while noting that meeting this threshold is not essential if TTFB does not prevent a site from doing well on the metrics that matter. See Optimize Time to First Byte.
| Metric | What it tells you | Good threshold or interpretation |
|---|---|---|
| LCP | How quickly the largest visible content element renders. | 2.5 seconds or less at the 75th percentile, assessed separately for mobile and desktop. See Largest Contentful Paint. |
| INP | How responsive the page is to user interactions. | 200 milliseconds or less at the 75th percentile, separately for mobile and desktop. See Web Vitals. |
| CLS | How much visible content shifts unexpectedly. | 0.1 or less at the 75th percentile, separately for mobile and desktop. See Web Vitals. |
| TTFB | How long it takes for the response to begin arriving after navigation starts. | A supporting diagnostic; 0.8 seconds or less is a rough target, not a Core Web Vital pass/fail threshold. See Optimize Time to First Byte. |
How do I test Core Web Vitals in a React app?
1. Choose routes and user flows
Test representative routes in a production-like build, not just the development server. Include initial navigation to pages that render meaningful content and the interactions users rely on, such as opening a menu, submitting a form, or changing a view. A route that looks fast on first load can still become unresponsive when its React components do work after an interaction.
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2. Run controlled lab checks
Use Lighthouse for repeatable checks as code changes. You can run it in Chrome DevTools, as an npm package, or in CI with Lighthouse CI. Chrome DevTools’ Performance panel can show Core Web Vitals as you load and interact with the page; WebPageTest can help reproduce a chosen device and network setup. The official Web Vitals guide describes these testing options.
Keep the comparison conditions stable. Record the URL, build, device emulation, network setting, cache state, and interaction tested. Re-run the same route and flow under the same setup after a change; otherwise, a changed result may reflect the test conditions rather than the code.
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Lighthouse reports LCP and CLS and uses Total Blocking Time (TBT) as a lab proxy for INP. TBT is useful for spotting main-thread blocking, but it is not the field INP value. A lab run cannot measure INP as a real user experiences it because INP depends on actual interactions. See Google’s guidance on lab and field measurement.
How do I measure real-user experience?
For a public page represented in the Chrome User Experience Report (CrUX), PageSpeed Insights can show field data alongside lab results. CrUX gives a broad view, but it may not provide the pageview-level detail needed to diagnose a particular regression.
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For ongoing, page-level telemetry, instrument real-user monitoring. Google’s web-vitals library wraps browser APIs and can report metrics such as LCP, INP, and CLS to an analytics endpoint. Its documentation includes examples using onCLS, onINP, and onLCP: Web Vitals.
Aggregate field measurements and check the 75th percentile separately for mobile and desktop. Field data reflects real devices, connections, page variations, and interactions; a lab run is controlled and repeatable, but cannot reproduce the full range of user conditions.
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How should I diagnose TTFB?
Measure navigation TTFB with browser timing data, Chrome DevTools, PageSpeed Insights, or the onTTFB function in web-vitals. Depending on the measurement context, TTFB can include redirect and connection-setup effects as well as time spent waiting for the response to begin. See Google’s TTFB guide and the Web Vitals documentation.
If server-side work may be responsible for a slow result, add the Server-Timing response header to expose backend stages. Compare lab and field readings with care: a lab may hit a warm server cache, test a final URL without redirects, or use a network unlike the one users have. Check less common routes or appropriate cache-bypassing conditions when investigating, while keeping the request representative of real use.
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Why are Lighthouse and PageSpeed Insights results different?
The tools may be showing different kinds of evidence. Lighthouse runs a controlled lab test; PageSpeed Insights can also show field data from CrUX when the page is represented there. A controlled run can differ from real-user results because of device and network mix, redirects, cache behavior, content variation, and interaction patterns. A single lab result is not proof that field data is wrong, nor does a good field aggregate explain every slow route.
For a client-rendered React route, a quick response containing initial markup does not guarantee that meaningful content will render quickly or that the page will respond promptly. The browser may still need to download and execute JavaScript before the content becomes useful. Google specifically notes the importance of low TTFB for single-page apps whose client rendering follows initial markup in Optimize Time to First Byte.
When readings conflict, inspect the conditions and the user journey: check which URL and redirects were tested, whether caches were warm, whether the same content was served, and whether the field population used different devices or performed interactions absent from the lab run. Then review TTFB alongside LCP, INP, and CLS rather than treating any one score as a verdict on the whole app.
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