Can JavaScript code splitting really enhance your crawl budget and improve your Core Web Vitals?

What is code splitting and why is Google talking about it now?

Code splitting involves breaking your JavaScript into several pieces (chunks) that load only when and where they are needed. Instead of serving a monolithic 500 KB JS bundle on every page, you load 50 KB on the homepage, 120 KB on product listings, and 80 KB + reCAPTCHA only on the contact form.

Google is not making this up — it is a common practice in modern web development (Webpack, Rollup, Vite handle it natively). What’s interesting is that Martin Splitt explicitly recommends it from an SEO perspective, signaling that Google is still seeing too many sites serving unnecessary JavaScript everywhere.

What is the real impact on crawling and indexing?

Googlebot must execute JavaScript to understand the content rendered on the client side. Every kilobyte of JS consumes processing time and crawl budget — especially on large sites.

A 150 KB reCAPTCHA script loaded on 10,000 pages when it’s only needed on 3 forms means 1.5 GB of wasted bandwidth and thousands of milliseconds of unnecessary parsing. The Google bot crawls shallower, indexes slower, and the Core Web Vitals (LCP, TBT, CLS) suffer.

Why is this statement important for SEO practitioners?

Many technical teams set up JS in a “one-size-fits-all” mode for ease of deployment. Modern CMS systems (WordPress with builders, Shopify with third-party apps) stack scripts indiscriminately.

Splitt officially confirms that this approach penalizes SEO. This is a compelling argument for development teams that are hesitant to implement conditional lazy loading or dynamic import. Google says: “Do it.”

• Reduction of page weight → improvement of LCP and TBT
• Optimization of crawl budget → deeper crawl on large sites
• Less blocking time → better user experience and positive UX signals
• Official Google argument to convince technical teams to prioritize code splitting
• Measurable SEO impact via PageSpeed Insights and the Core Web Vitals reports in the Search Console

Is this recommendation aligned with practices observed in the field?

Absolutely. Technical audits regularly reveal sites serving 400-800 KB of JS on each page, of which 60-70% never execute. Poorly configured Google Tag Manager, ad pixels, chatbots, social widgets, analytics tracking — everything loads everywhere.

Sites that have migrated to a code-split architecture (Next.js, Nuxt with automatic splitting, or properly configured Webpack) see 20-40% gains in Core Web Vitals metrics. Ranking does not always follow immediately, but bounce rates decrease and session time increases — two UX signals that Google values indirectly.

What nuances should be added to this statement?

Splitt mentions “recommended,” not “mandatory.” For a site with 50 pages and a total of 120 KB of JS, the effort may not be worth it — the technical cost of refactoring exceeds the marginal SEO gain.

However, for an e-commerce site with 10,000 SKUs or a media site with 100,000 articles, it’s critical. The crawl budget becomes a limiting factor, and every millisecond of TBT saved multiplies across thousands of pages.

Importantly, poorly implemented code splitting can fragment loading and create HTTP request waterfalls that degrade performance instead of improving it. [To be verified] that your implementation does not create more problems than it solves — test with WebPageTest and Lighthouse before/after.

In what cases does this rule not apply or need adjustments?

If your site uses a CDN with HTTP/2 or HTTP/3 and a well-configured cache, the cost of universal JS loading is partially mitigated. Browsers cache the bundle and rarely reload it. But Googlebot does not always cache in the same way a returning visitor does — it crawls “cold.”

Another case: Single Page Applications (SPAs) where all JS is necessary from the first interaction. Here, code splitting occurs more at the route level (lazy-load of views that do not appear on the first screen). This is technically more complex and requires a framework that handles it natively (React.lazy, Vue async components).

What are the concrete steps to implement code splitting?

First step: audit current JS. Use Coverage in Chrome DevTools (Cmd+Shift+P → Show Coverage) to identify the percentage of unused code on each type of page. If you are above 50% of unexecuted code, code splitting is a priority.

Second step: map scripts by page type. reCAPTCHA only on /contact, product configurator scripts only on /configurator, video analytics only on /videos/*. Create a clear dependency matrix.

Third step: use the native techniques of your stack. Webpack offers import() dynamic, Next.js has next/dynamic, Nuxt has components: { lazy: true }. If you are on WordPress, plugins like Autoptimize or WP Rocket allow for basic conditional JS — but for real code splitting, refactoring is often necessary.

What mistakes should be avoided when implementing code splitting?

Do not over-fragment. If you create 50 micro-chunks of 5 KB each, you multiply HTTP requests and create a connection overhead that cancels out the gains. Aim for chunks of 30-100 KB minimum, grouped by logical functionality.

Avoid blind splitting without measuring. Some scripts should remain in the main bundle (critical polyfills, feature detection scripts). If you split too aggressively, you risk FOUC (Flash of Unstyled Content) or broken interactions before the chunk loads.

Be cautious with preloading (<link rel="preload">) critical chunks. A lazy-loaded chunk that blocks a key interaction (CTA button, dropdown menu) must be preloaded intelligently. Otherwise, you degrade UX instead of improving it.

How can you verify that the implementation works and generates SEO gains?

Monitor Core Web Vitals in the Search Console (Essential Web Signals report). Compare before/after on a representative sample of pages. Good code splitting should reduce TBT by 30-50% and improve LCP by 10-20%.

Use Screaming Frog in JavaScript mode to crawl your site like Googlebot. Verify that critical content displays well without waiting for non-critical chunks to load. If a page becomes empty without JS, code splitting will not solve anything — it’s an SSR architecture issue.

Measure the impact on crawl budget via server logs or the Search Console (crawling statistics). A well-optimized site sees an increase in the number of pages crawled per day and a reduction in the average response time perceived by Googlebot.

• Audit JavaScript with Coverage in Chrome DevTools and identify unused code (goal: <30% of unexecuted code)
• Create a scripts/pages matrix to define real dependencies
• Implement code splitting via dynamic import() or native framework tools
• Test performance before/after with Lighthouse and WebPageTest (minimum 3 runs for statistical reliability)
• Monitor Core Web Vitals in the Search Console and check improvement over 4-6 weeks
• Verify JavaScript rendering with Screaming Frog or Google Search Console (live URL test)