Cloudinary now transforms assets with AI, slashing manual coding for dynamic images

<img src="https://res.cloudinary.com/demo/image/upload/t_product_hero/sample.jpg" />

Cloudinary’s shift from a basic CDN to an AI-driven asset management system means its core utility now lies in intelligent image processing—like generative fill and context-aware cropping that adjusts to the content rather than just the frame’s center. Those still using it solely for static image hosting risk overlooking its full potential, akin to relying on a CDN for basic file delivery instead of its advanced optimization layers.

How AI reduces frontend complexity in Next.js

When developers offload transformation logic to Cloudinary’s URL parameters, they eliminate repetitive frontend code. For example, in Next.js, instead of writing CSS to handle responsive object-fit or manually adjusting thumbnails for different viewports, developers embed transformation strings directly into URLs. A single high-res image now supports multiple sizes via parameters like g_auto (gravity auto) and c_fill, letting AI dynamically identify and crop the most engaging area—no manual adjustments needed.

Here’s how it simplifies product imagery:

const getOptimizedImage = (publicId, width, height) => {
  return `https://res.cloudinary.com/demo/image/upload/w_${width},h_${height},c_fill,g_auto,f_auto,q_auto/${publicId}`;
};

The f_auto,q_auto flags ensure the browser selects AVIF or WebP without conditional checks, reducing backend overhead.

Does AI editing replace manual work entirely?

Cloudinary’s Generative AI tools like background removal (e_bgremoval) replace hours of Photoshop work or Python scripting with rembg. For Cursor-based frontends, pushing image logic to Cloudinary’s URL-based transforms—rather than embedding CSS or React logic—cleans up the codebase. This shift improves Largest Contentful Paint (LCP) scores by offloading heavy processing to the server.

Managing AI-driven transformations effectively

Transitioning requires careful planning. AI Credits for generative effects (like fill or removal) are billed separately from bandwidth. High-traffic pages with repeated AI transforms risk quota depletion; caching results or using permanent URLs is essential. Another challenge is layering precision, where AI-generated overlays may misalign due to varying source image aspect ratios. Testing multiple dimensions before deployment helps mitigate this. Finally, URL bloat becomes problematic when stacking multiple AI effects. Cloudinary’s Named Transformations (e.g., t_product_hero) let teams define reusable AI logic in the dashboard, keeping code concise and enabling design updates without redeploying the app.