CatalogX vs FASHN.ai: AI Virtual Try-On Comparison for Fashion Sellers

What FASHN.ai does well

Credit where it is due: FASHN.ai is a serious player in the virtual try-on space. It offers a clean web app and a well-documented API that lets developers integrate garment try-on into their own products. Upload a photo of a person and a photo of a garment, and its models render the garment onto the person with impressive fidelity for standard Western apparel — t-shirts, jeans, jackets, and dresses. If you are a SaaS company building try-on into a shopping app, or a Western brand with in-house developers, FASHN.ai's API-first approach makes sense.

But that API-first, developer-first identity is exactly what makes it a mismatch for the typical Indian fashion seller. Most boutique owners and marketplace resellers do not want an API; they want finished, listing-ready images. And they are not selling t-shirts and jeans — they are selling sarees, lehengas, kurta sets, and anarkalis, garments whose structure a Western-trained try-on model simply does not understand.

The ethnic wear gap

Virtual try-on AI works by learning how garment categories behave on a body: where a t-shirt's shoulder seams sit, how denim creases at the knee, how a dress falls from the waist. FASHN.ai's training and product design center on these Western categories. A saree breaks every one of those assumptions. It is a single six-metre unstitched cloth whose appearance depends entirely on draping — pleats at the waist, the pallu over the shoulder, the fall across the body. A lehenga's flare, a dupatta's placement, an anarkali's flowing silhouette: none of these map onto a tops-and-bottoms model. The result, when sellers try, is warped pleats, missing pallus, and drapes that no Indian customer would recognize as real.

CatalogX approaches this from the opposite direction. It was built for the Indian fashion market first, with garment-type intelligence across 14+ categories that includes dedicated handling for sarees, lehengas, kurtas, and other ethnic wear. The AI detects the garment type and applies category-specific draping, fit, and styling rules — so a Kanjeevaram saree renders with proper pleats and pallu, and a bridal lehenga keeps its volume and embellishment detail. For sellers on Amazon India, Flipkart, Myntra, and Meesho, where ethnic wear is one of the largest fashion categories, this is not a nice-to-have. It is the whole job.

Models your customers recognize

There is a second, subtler gap: the models themselves. Conversion research in Indian e-commerce consistently shows that shoppers respond better to models who look like them — familiar skin tones, features, and styling. FASHN.ai largely expects you to bring your own model imagery, and its stock options skew Western. CatalogX ships a library of 40+ diverse models with strong Indian representation across ages, skin tones, and body types, plus full control over pose, angle, and expression. You can keep one model consistent across your entire catalog, which makes a storefront look like a professional photoshoot rather than a patchwork of AI experiments.

FASHN.ai's paid plans start at $29 per month — roughly ₹2,400/month, or close to ₹29,000 a year — billed in US dollars to an international card. For a funded startup integrating an API that is a rounding error; for a boutique seller in Jaipur uploading thirty listings a month, it is a significant fixed cost before a single sale, and USD billing itself is a hurdle for many Indian sellers without international payment cards.

CatalogX starts free: 5 image generations on signup, no credit card required, so you can test the output on your own sarees and kurtas before paying anything. Paid credit packs start at ₹299 one-time — working out to roughly ₹10-50 per finished image depending on the pack — with Indian payment methods and no recurring subscription lock-in. You pay when you need images, not every month regardless.

The hidden cost: everything after generation

Price-per-image is only half the story, because a raw try-on generation is not a marketplace listing. Amazon India requires a pure white background on the main image. Flipkart and Myntra each have their own dimension and format specs. Zoom views demand high resolution. If the same kurta ships in four colors, you need four sets of images. With FASHN.ai, every one of those steps happens in some other tool: a background remover here, an upscaler there, a resizing script, a design app for variants. Each extra tool adds cost, time, and quality risk.

CatalogX bundles the whole pipeline. Background removal is free, HD upscaling goes up to 4x for zoom-ready detail, color variants generate from a single source image, and one-click marketplace export outputs correctly sized files for 10+ platforms including Amazon, Flipkart, Myntra, and Instagram. The thing you download is not an AI experiment — it is the file you upload to your seller dashboard.

Which should you choose?

Choose FASHN.ai if you are a developer or a company that needs a try-on API to embed in your own product, and your catalog is Western-style apparel. Choose CatalogX if you are a fashion seller — especially in India — who needs finished, marketplace-compliant product images of real garments, including ethnic wear, at a price that scales from your first listing to your thousandth. Both tools are good at what they are built for. They are just built for different people, and if you are reading this from a seller dashboard, CatalogX was built for you.