Ello isn't trying to be the cheapest virtual try-on app: it's built to be the one that pays for itself. Apparel-first rendering that goes deep on clothing, accessories on the same engine, results in seconds, and ROI you can audit against a control group of your own shoppers.
"Tries on your whole catalog" sometimes reads like a trade-off: as if covering hats and bags must mean shallower clothing rendering. It's the reverse. Clothing is the hard problem, so clothing is where the engine's depth lives: product-aware rendering that treats a bias-cut dress, raw denim, and a chunky knit as different problems, and keeps the shopper's own photo framing intact. Accessories run on that same apparel-grade engine: covering a hat or a bag costs the dress nothing.
Apparel categories Ello renders in depth: dresses and sets, denim, corsetry and lace, knitwear and sweaters, outerwear and jackets, activewear, menswear, and graphic tees (front and back from one generation). The detail-heavy categories: lace, corsetry, textured denim, knit stitchwork: are exactly where most try-on tools stay "simple"; they are where Ello leans in. See a live render.
The shopper uploads a photo and watches the result arrive in the same session, right on the product page: fast enough to try the next item, build a full outfit with Complete the Look, and add to cart while the intent is hot. No emailed results, no come-back-later.
Real, individual client numbers (results vary): a client on the $97/mo plan attributed $408 in their first 15 days; another saw $976 attributed across 288 try-on sessions (5.6% try-on-to-purchase); a third measured a 68% conversion-rate increase the week after installing. See our Results page.
Attribution alone can flatter any tool, so Ello ships the check on itself: a built-in A/B holdout holds back a slice of your own shoppers who never see the widget, and the dashboard compares the two groups on conversion rate, order value, and returns: with statistical confidence, and lift claimed only when the sample supports it. Every attributed sale lands in a receipts ledger tied to a real order, with discounts netted out. The full attribution rulebook is public at /proof-engine.
No: it's the opposite of a trade-off. Apparel is Ello's specialty: the engine is product-aware, adjusting how it renders drape, texture, and layering by garment type, and it handles the categories most try-on tools avoid: lace, corsetry, denim texture, knit detail. Accessories run on that same apparel-grade engine; covering a hat or a bag costs the dress rendering nothing. Breadth is what the depth makes possible, not a shortcut around it.
Seconds, not minutes: and in the session, right on the product page. The shopper uploads a photo, watches the render arrive, and can keep going: try the next item, build a full outfit with Complete the Look, add to cart. No emailed results, no come-back-later.
Two layers. An attribution pixel ties try-on engagement to real orders: that's where the Results numbers come from, with discounts netted out. And a built-in A/B holdout can hold back a slice of your own shoppers as a control group, comparing conversion, order value, and returns between the two groups with statistical confidence: lift is only claimed when the sample supports it. The full rulebook is public at /proof-engine.
Because it's built to pay for itself. Most try-on apps compete on the lowest monthly price; Ello is priced for return on investment. If virtual try-on recovers even a couple of returns or adds a couple of sales a month, it has already covered its cost: and our client dashboards show it doing much more than that.
Start on the free plan (10 try-ons/month) and watch what it does on your real product pages before paying a cent. Paid plans start at $49/month. The question isn't the sticker price: it's whether the returns saved and conversions added beat that.