Yes—but an A/B test does not automatically make a site slower. Its effect depends on how visitors are assigned to variants and what each variant changes. A client-side tool that delays showing the page can hurt Largest Contentful Paint (LCP); content inserted or moved by a variant can affect Cumulative Layout Shift (CLS). To find out whether a test is responsible, compare real-user results by experiment group rather than relying on one lab run.
How A/B testing can change Core Web Vitals
Google’s current Core Web Vitals are LCP, Interaction to Next Paint (INP), and CLS. A/B testing can affect them through the experiment’s implementation or the variant itself; the mere presence of a test does not establish a uniform performance penalty. Google’s guidance cautions that the performance cost of a test should be weighed against its potential benefit (A/B testing guidance).
LCP: a delayed first display
Some client-side testing tools wait to show the page until they have selected and applied a variant. That can delay the visibility of the page’s main content and worsen LCP. Server-side assignment can avoid this particular client-side delay mechanism because the variant can be selected before the page is delivered. It does not guarantee good LCP: the content and implementation still matter.
CLS: content that shifts the page
A variant may add, remove, or reposition elements. If content arrives after the initial layout without space reserved for it, existing content can move and contribute to CLS. A stable variant should account for the space its elements occupy rather than inserting them in a way that unexpectedly pushes other content around.
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INP: measure before attributing a change
INP measures responsiveness to user interactions. A test could affect it if its code adds main-thread work or changes an interaction, but the fact that a test is running is not evidence that INP has worsened. Compare real-user interaction data by variant and inspect the relevant code before assigning a cause. INP replaced First Input Delay (FID) as a Core Web Vital in March 2024 (Google’s INP announcement).
What counts as a good result
Google’s current Web Vitals guidance defines a good result at the 75th percentile as follows. Evaluate mobile and desktop separately; a combined score can conceal poor results for one device group (Web Vitals).
| Metric | Good result at the 75th percentile | What it reflects |
|---|---|---|
| LCP | 2.5 seconds or less | How quickly the main page content becomes visible. |
| INP | 200 milliseconds or less | How responsive the page is to user interactions. |
| CLS | 0.1 or less | How much unexpected layout movement occurs. |
How to measure an experiment fairly
Compare control and treatment among real users, and record each visitor’s assigned experiment group or version with the performance observation. Google recommends setting the group on the server and avoiding client-side experimentation tools that block rendering (A/B testing guidance).
- Record assignment with the pageview. Set the experiment group on the server where possible, then attach the group or version to your analytics or real-user monitoring (RUM) data.
- Compare equivalent groups. Examine LCP, INP, and CLS for control and treatment, and segment results by mobile and desktop. Use the 75th percentile for each device group.
- Use lab tests to investigate changes. Run repeatable diagnostics during development to identify likely regressions and inspect variant-specific code, loading behavior, and layout.
- Check field experience before drawing a conclusion. Real-user data captures variation in devices, networks, caching, interactions, and layout changes across a page session that a single lab run cannot represent.
Lab tests and field data answer different questions
Lab tools such as Lighthouse help reproduce conditions and diagnose possible causes. A single run is not a measure of how all visitors experience an experiment: results can vary with device, network, cache, and variant content. A conventional lab run without interactions cannot directly measure INP, and a run that ends early can miss layout shifts later in the session. Lighthouse user flows can script interactions, but they complement rather than replace field measurement.
CrUX and Google’s Core Web Vitals tools provide field-performance insight, but CrUX does not offer the per-pageview detail often needed to diagnose an experiment quickly. For experiment-level analysis, site-owned RUM can associate performance observations with the recorded variant and give a more detailed view of individual pageviews (Getting started with Web Vitals measurement).
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Ways to reduce the risk of a test
- Use server-side assignment where practical, so variant selection does not require a client-side render delay.
- Limit the experiment tool to the pages being tested and, when appropriate, a subset of users.
- Design variants to avoid unexpected layout movement, including by reserving space for content that appears later.
- Keep an experiment only as long as needed and remove its code when the test is complete.
These practices reduce avoidable exposure; they do not replace measuring the actual variants. Google’s guidance recommends limiting experiment scope and duration as well as understanding how changes are applied (A/B testing guidance).
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