The best examples of frontend performance bottlenecks: 3 key examples every team should know

When people ask for examples of frontend performance bottlenecks: 3 key examples, JavaScript is almost always in the first slot. Modern frameworks make it easy to ship a single-page app; they also make it dangerously easy to ship way too much JavaScript.

The pattern is simple: every new dependency, component library, and tracking script adds weight. That weight has to be downloaded, parsed, compiled, and executed on the main thread. On a MacBook Pro, it feels “fine.” On a mid-range Android from 2019, it feels like your site is loading through wet cement.

Real examples of JavaScript-driven frontend performance bottlenecks

Let’s start with several concrete examples of how JavaScript becomes a bottleneck in real apps.

Bloated SPA dashboard (React/Vue/Angular)
A B2B analytics dashboard ships a 3.5 MB JavaScript bundle on first load. The team proudly uses tree-shaking and code splitting, but they also:

• Pull in a full charting library for every route, even simple tables
• Bundle the entire date-picker library (with time zones and locales) for one filter
• Include a UI component library where they use 10% of the components

On a modern desktop, First Contentful Paint (FCP) looks okay, but Time to Interactive (TTI) is terrible. The browser spends 3–5 seconds just parsing and executing the bundle. During that time, the UI is visible but unresponsive. This is a textbook example of a frontend performance bottleneck: the CPU is busy burning through JavaScript instead of responding to input.

Marketing site with five analytics tags and three A/B testing tools
Another classic example of a frontend performance bottleneck is the over-instrumented marketing site. The page itself is simple, but:

• Google Analytics, a heatmap tool, a customer chat widget, two A/B testing SDKs, and a CRM tracker all load on every page
• Most of them run on the main thread, patching history, fetch, or XMLHttpRequest
• Some inject extra DOM nodes and styles on every route change

The result: layout shifts, delayed input handling, and random jank that the frontend team can’t reproduce locally because they don’t test with all tags enabled.

Client-side rendering for everything
A news site ships a React SPA that renders 100% of content on the client. The HTML from the server is basically an empty shell with a root <div>. This is another strong example of frontend performance bottlenecks in the wild:

• The browser has to download and execute the JavaScript before any meaningful content appears
• On slow networks and devices, users stare at a blank page or spinner for several seconds

Google’s Chrome team has repeatedly highlighted the cost of JavaScript parsing and execution in performance guidance (see web.dev). The more logic you push to the client, the more likely you are to hit this bottleneck.

How to recognize this bottleneck in your own app

Signs your JavaScript is the main example of a frontend performance bottleneck:

• Long “Script Evaluation” or “Parse Script” times in Chrome DevTools Performance panel
• Main thread is blocked for hundreds of milliseconds at a time
• High Total Blocking Time (TBT) or poor Interaction to Next Paint (INP) in Core Web Vitals
• Users on mid-range Android devices complain that the app is “laggy” or “frozen”

If you’re looking for examples of frontend performance bottlenecks: 3 key examples that show up in audits, this is usually number one.

2. Layout thrashing and rendering jank: examples include carousels, modals, and sticky headers

The second big category of bottlenecks lives in layout and rendering. Even if your JavaScript bundle is under control, you can still tank performance by forcing the browser to recalculate layouts and repaint too often.

These examples of frontend performance bottlenecks are sneaky, because the page might load quickly but feel awful to use.

Real examples of layout and rendering bottlenecks

Infinite scroll feed with expensive layout on each batch
A social feed loads 20 items at a time. Each item contains:

• An image with no fixed dimensions
• Text that changes height as fonts load
• Dynamic badges that appear based on user status

As the user scrolls, the app:

• Appends DOM nodes
• Reads layout properties like offsetHeight
• Then writes new styles based on those values

This read–write–read–write pattern is classic layout thrashing. The browser repeatedly recalculates layout and triggers style recalculation. On lower-end devices, scrolling stutters and frames drop below 30 FPS.

Sticky header and footer with position: fixed on complex pages
A content site adds a sticky header, sticky newsletter bar, and sticky chatbot bubble. Each has box-shadows, gradients, and blur effects. On scroll, JavaScript updates their styles to shrink, fade, or move.

The combination of fixed-position elements, heavy effects, and JS-driven style changes triggers frequent paints. This is a very common example of a frontend performance bottleneck: the GPU and CPU are constantly repainting large areas of the screen.

CSS animations on layout properties
A modal animates from the bottom of the screen using changes to top and height. At the same time, the background content shifts slightly to indicate focus. Because these are layout properties, the browser has to recalculate layout on every frame of the animation.

Modern performance guidance (again, see web.dev’s animation best practices) recommends animating transform and opacity instead. When teams ignore this, they create one of the best examples of frontend performance bottlenecks at the interaction level: animations that drop frames and feel choppy.

Third-party widgets that rewrite the DOM
A chat widget injects an <iframe> plus a bundle of scripts. On every route change, it:

• Scans the DOM for specific elements
• Inserts tracking attributes
• Rebuilds parts of the DOM tree

If you’re seeing random layout shifts and scroll jumps, this kind of widget is often the culprit. It’s a very real example of a frontend performance bottleneck that ships in production without the core team’s full understanding of its cost.

How to spot layout and rendering bottlenecks

You can confirm this category of examples of frontend performance bottlenecks with:

• Chrome DevTools Performance panel → Look for long “Recalculate Style,” “Layout,” and “Paint” tasks
• Core Web Vitals → Poor Cumulative Layout Shift (CLS) and INP scores
• Rendering stats → Use about://tracing or Chrome’s Rendering tools to see paint flashing and layer borders

If scrolling feels janky or animations are choppy even after you optimize JavaScript size, this is likely your bottleneck.

3. Network misuse: the underrated example of frontend performance bottlenecks in 2024–2025

The third category is less glamorous but just as damaging: how you use the network. In 2024–2025, users expect fast sites even on mobile networks. Yet many apps still load like it’s 2010.

This is where some of the best examples of frontend performance bottlenecks show up in real audits: oversized images, unoptimized fonts, and chatty APIs.

Real network-related examples of frontend performance bottlenecks

Hero images that weigh more than the rest of the page
A landing page uses a 4000×3000 PNG as its hero background. The visible area on most screens is closer to 1400×900. No responsive images, no compression, no lazy loading.

On a 4G connection, that one image can add multiple seconds to Largest Contentful Paint (LCP). This is one of the clearest examples of a frontend performance bottleneck that designers and marketers can fix without touching JavaScript at all.

Blocking web fonts with no fallback
A news site uses three custom web fonts (regular, bold, and italic) across multiple weights. Fonts are loaded with font-display: block, which hides text until the fonts arrive.

On a slow connection, users see blank areas instead of content for 2–4 seconds. Google’s performance guidance recommends font-display: swap or optional to avoid this. Ignoring that advice is a textbook example of frontend performance bottlenecks tied directly to network and rendering.

Waterfall API calls on page load
A dashboard makes this sequence on load:

• Fetch user profile
• Use profile ID to fetch settings
• Use settings to fetch feature flags
• Use flags to decide which widgets to show, then fetch data for each widget

Each step waits for the previous one to finish. This waterfall pattern is a very real example of a frontend performance bottleneck that’s entirely self-inflicted. Many of these calls could be parallelized or pre-fetched.

Polling instead of subscriptions
A live-updating table polls the server every 3 seconds for updates. Each poll returns the full dataset, not just changes. On a busy dashboard with multiple widgets, you end up with a storm of network requests and JSON parsing.

The result: wasted bandwidth, CPU overhead, and random UI jank whenever the responses arrive. This is common in internal tools and admin dashboards—a quiet but nasty example of a frontend performance bottleneck.

Modern trends that make network bottlenecks worse

Several 2024–2025 trends amplify these examples of frontend performance bottlenecks:

• Heavier design systems and images: High-density marketing visuals, auto-generated illustrations, and video backgrounds are everywhere.
• More third-party scripts: Ad tech, analytics, personalization, and AI assistants all add their own network and CPU costs.
• Global audiences: Your users might be on fast fiber in San Francisco—or on a congested 4G network in a different region. The same page can feel fast for one and unusable for the other.

Organizations like the W3C and browser vendors have been pushing performance guidance for years (see the W3C Web Performance Working Group and resources on web.dev). Yet real-world data still shows many sites failing Core Web Vitals, especially LCP and INP.

Pulling it together: how to use these 3 key examples in your own debugging

By now, you’ve seen multiple examples of frontend performance bottlenecks: 3 key examples that map to real-world problems:

• JavaScript overload
• Layout and rendering jank
• Network misuse

But in practice, your app probably has a mix of all three. A slow page is rarely caused by a single line of code; it’s usually a pile-up of small decisions.

Here’s a practical way to apply these examples without turning this into a months-long performance crusade.

Step 1: Measure like a skeptic

Start with field data, not just lab tests. Use:

• Real User Monitoring (RUM) tools to see Core Web Vitals for actual users
• Chrome User Experience Report (CrUX) data, where available

Focus on LCP, INP, and CLS. They map nicely to the three categories of bottlenecks we just walked through.

Step 2: Profile one slow flow at a time

Pick a single user journey that matters: homepage → product page → checkout, or login → dashboard. For that flow:

• Record a performance trace in Chrome DevTools
• Capture screenshots and CPU profiles

Then match what you see to the examples of frontend performance bottlenecks in this article:

• Long script tasks and high CPU usage → JavaScript overload
• Frequent layout and paint events → layout and rendering bottlenecks
• Long gaps waiting on network → network misuse

Step 3: Fix the highest-impact example of a frontend performance bottleneck first

Instead of chasing micro-optimizations, pick the one bottleneck that clearly dominates:

• If a single library adds 1+ second of script evaluation, refactor or lazy-load it
• If a hero image is 3 MB, fix that before worrying about shaving 10 KB off your bundle
• If your layout shifts three times before settling, fix dimensions, fonts, and third-party injections

This is where the “3 key examples” framing is actually useful: it keeps you from getting lost in noise.

FAQ: common questions about real examples of frontend performance bottlenecks

What are some real-world examples of frontend performance bottlenecks I should check first?

Start with these:

• Oversized JavaScript bundles (3 MB+), especially on first load
• Hero images larger than 500–800 KB without compression or responsive sizing
• Multiple analytics and A/B testing scripts loaded on every page
• Web fonts that block text rendering and use font-display: block
• Infinite scroll lists that recalculate layout on every scroll event
• Chat widgets and pop-ups that inject heavy scripts and rewrite the DOM

These are the best examples of frontend performance bottlenecks that show up again and again in audits.

How do I know if JavaScript is the main example of a frontend bottleneck?

Open Chrome DevTools, record a performance trace, and look at the main thread. If you see long purple (scripting) bars, especially during initial load or user interactions, JavaScript is likely your primary example of a frontend performance bottleneck. High Total Blocking Time (TBT) and poor Interaction to Next Paint (INP) in Core Web Vitals are strong indicators.

Are images still a big example of frontend performance bottlenecks in 2024–2025?

Yes. Even with modern formats like WebP and AVIF, many sites still ship oversized or uncompressed images. Large hero images, background images loaded above the fold, and unlazy-loaded below-the-fold images are consistent examples of frontend performance bottlenecks that hurt LCP and bandwidth.

How do third-party scripts contribute to frontend performance bottlenecks?

Third-party scripts often:

• Run on the main thread
• Add extra network requests
• Modify the DOM and styles

Because you don’t control their internals, they can easily become a hidden example of a frontend performance bottleneck. The safest approach is to audit them regularly, load them asynchronously where possible, and remove anything that doesn’t clearly earn its place.

What’s the fastest way to start reducing frontend performance bottlenecks?

Use the real examples in this article as a checklist. Measure one key flow, identify whether JavaScript, layout, or network is your dominant bottleneck, and fix one high-impact issue in that category. Then re-measure. Iterating like this is far more effective than trying to optimize everything at once.

If you treat these examples of frontend performance bottlenecks: 3 key examples as a diagnostic lens rather than a trivia list, you’ll find the real problems in your app much faster—and your users will feel the difference.