— The wardrobe app · Updated May 2026
Wardrobe app, reimagined.
Older wardrobe apps catalogue what you already own. Capsule Wardrobe AI builds what you don't — with photorealistic AI try-on, real shoppable pieces, and capsule logic that multiplies your outfit count. PWA on every device. 1 free try-ons, no signup.
1 free AI try-on · No signup, no card
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What this wardrobe app does
AI try-on for every piece
Upload one photo. See yourself wearing every garment in the curated capsule library — photorealistic, 10 seconds per generation, fashion-trained 864×1296 model. The single biggest gap in older wardrobe apps; the one feature that makes online clothes-shopping work.
Capsule builder with real outfit logic
Every piece you accept multiplies with the rest. 5 pieces = 8 outfits. 12 pieces = 40. 30 pieces = 100+. The HUD shows the math live as you build. Named outfit recipes ('The Drake's Office', 'The Aimé Leon Dore Saturday') anchor the suggestions in real menswear vocabulary.
Save for later, skip, and accepted state
Three actions per garment: add to capsule, save for later, skip. The state persists across sessions (browser storage, no signup needed). Come back tomorrow and pick up exactly where you left off. No 'lost progress' moment.
Web PWA — no app install required
Works in every browser on iOS, Android, and desktop. Installs as a home-screen icon if you want, but doesn't have to. No App Store, no Play Store, no signup gate. Open URL → upload photo → start building.
How this wardrobe app differs from the others
There are dozens of wardrobe apps. The honest comparison: most of them solve a different problem than this one. Below: how Capsule Wardrobe AI compares to the category leaders.
Whering / Indyx / Cladwell
They
Closet-cataloguing first. Photograph your existing clothes, get outfit suggestions from what you already own.
Us
Capsule-building first. Curated library of new shoppable pieces, AI try-on before you spend.
Stylebook
They
iOS-only spreadsheet-grade tracking — every garment tagged, every wear logged, cost-per-wear calculated.
Us
PWA on every device. Less analytics depth, more shopping-decision power. Different jobs.
Raw AI try-on tools (FitRoom, Krea, etc.)
They
AI try-on as a single feature, no capsule logic, no curation, no shopping integration.
Us
Try-on tied to a curated capsule of real shoppable pieces. The full loop, not the demo.
Pronti / Acloset
They
AI outfit suggestions from photos of your closet.
Us
Curated capsule from real brands, with try-on visualization. We work with what you don't yet own; they work with what you do.
See it work for yourself
Upload one photo, build a capsule, see the AI try-on output.
Open the wardrobe appWhat the wardrobe app costs
Free
$0
3 AI try-ons. Browse the full library.
- 3 photorealistic AI try-ons
- Curated capsule library
- Outfit recipe view
- No signup, no card
Pro
$8/month
Unlimited try-ons. Full capsule export.
- Unlimited AI try-ons
- All free tier features
- Capsule PDF export
- Save outfit recipes
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Frequently asked questions
What is a wardrobe app?
A wardrobe app is a tool that helps you organise, plan, or shop for your clothes — usually through one of three approaches: (1) cataloguing your existing closet (Whering, Stylebook), (2) suggesting outfits from your existing closet (Indyx, Cladwell, Pronti), or (3) building a new wardrobe of pieces you don't yet own with curated recommendations (Capsule Wardrobe AI). Each solves a different problem; the right one depends on what you're trying to do.
What's the best wardrobe app in 2026?
It depends on the job. For curating a new wardrobe of shoppable pieces with AI try-on visualization, Capsule Wardrobe AI is built for that case specifically. For deep tracking of an existing closet (cost-per-wear, outfit logging, packing lists), Stylebook still leads despite the iOS-only and dated UX. For social/community features and outfit-from-existing-closet logic, Whering or Indyx. We have an honest ranking on /best-capsule-wardrobe-app.
Can a wardrobe app actually save me money?
Yes — but indirectly. The way wardrobe apps save money isn't by finding deals; it's by reducing the number of bad purchases. AI try-on per-piece dramatically lowers the rate of buying something that doesn't actually work on your body. Capsule logic prevents buying duplicates and pieces that don't combine with the rest of your wardrobe. The average user reports 30–40% reduction in wardrobe waste after switching to a capsule-driven approach.
How does the AI try-on actually work?
You upload one full-body photo (processed in-memory, never stored or trained on). The photo plus the garment image goes to our fashion-trained 864×1296 model, which generates a photorealistic image of you wearing the garment in roughly 10 seconds. The output is shown side-by-side with the garment specs and shopping link. The generation is sharp enough to make actual buying decisions — fabric drape, fit on shoulders, length on torso are all visible.
Is the wardrobe app free?
The first 3 AI try-ons are free, no signup, no credit card. Browsing the curated capsule library is free indefinitely. After 3 try-ons, the Pro tier is $8/month and unlocks unlimited generations, the outfit recipes view (every combination from your accepted pieces), and capsule export. Most users get the value they need within the free tier; the Pro tier is for active wardrobe-builders.
Do I need to download an app?
No. We're a PWA (progressive web app), which means it works in any browser on iOS, Android, and desktop. You can install it as a home-screen icon if you want — modern PWAs feel identical to native apps — but you don't have to. This avoids the App Store gating and the cold-start signup that most native wardrobe apps require.
Does the wardrobe app work for women and men?
Both. The current launch focus is menswear — that's where the curation is deepest and the editorial voice is strongest. Womenswear is live and growing weekly. Plus-size, petite, and demographic-specific lines (over 50, postpartum) are all curated. The AI try-on works equally well across body types because the underlying model is fashion-trained on diverse imagery, not stock-photo bodies.