Drape Try-On vs FASHN AI — Spec Comparison
Same model. Different products. Here is the spec sheet.
How do Drape Try-On and FASHN AI compare?
Drape Try-On and FASHN AI use the identical underlying model (FASHN v1.6 on fal.ai), so per-image generation quality, resolution, and latency are the same. They differ entirely in the product layer: Drape Try-On is a hosted consumer SaaS with a polished mobile Studio, AI Stylist chat, gallery, regenerate-with-seed, and a free 3-credit tier. FASHN AI is a raw developer API with no UI, no auth, no storage, and per-image billing through fal.ai. For end users, Drape is the only sensible choice. For engineering teams integrating try-on into a larger product, FASHN AI direct (or Drape’s Pro API plan) is the better fit.
- Same model under the hood — generation quality is identical.
- Drape Try-On adds Studio UI, gallery, AI Stylist, regenerate, mobile flow.
- FASHN AI direct is API-only — you build everything around it.
- Free tier: Drape gives 3 lifetime credits; FASHN inherits fal.ai trial credits.
- Drape Pro plan ($39/mo) exposes a managed REST API for e-commerce.
Feature matrix
| Feature | Drape Try-On | FASHN AI (direct) |
|---|---|---|
| Underlying model | FASHN v1.6 (fal.ai) | FASHN v1.6 (fal.ai) |
| Generation quality | Identical | Identical |
| Max resolution | 864 × 1296 | 864 × 1296 |
| Typical latency | ≈12 seconds | ≈12 seconds |
| Web UI | Polished mobile-first Studio | None (API only) |
| AI Stylist chat | Included | No |
| Saved gallery | Yes (per account) | Build it yourself |
| Regenerate with seed | One click | API parameter |
| Auth & storage | Managed (Supabase) | You build it |
| Free tier | 3 lifetime try-ons | fal.ai trial credits |
| Paid plans | $12 → $39 / month | Per-image (≋ $0.075) |
| Commercial licence | Included on Standard & Pro | You handle licensing terms |
| REST API for integrations | Pro plan ($39/mo) | Yes (native) |
| Best for | End users, creators, small brands | Engineering teams |
indicates the winner on that row. indicates a tie.
Why the model is the same
Drape Try-On calls fal.ai’s hosted FASHN v1.6 endpoint. So does any direct FASHN AI user. The endpoint produces the same image regardless of which client invokes it, given the same inputs and seed. Per-image quality therefore depends entirely on input photos, not on which product you used to call the API.
The interesting question for users is not "which model" but "what wraps the model." For most non-engineering users, the wrapper is the entire product.
What Drape Try-On adds
Drape Try-On is a complete consumer-grade product: a luxury dark-UI Studio, one-click upload-and-generate flow, the AI Stylist chat module for outfit suggestions, a gallery of saved looks, a regenerate button with seed control, friendly error messages translated from raw API responses, full mobile responsive design, a free Starter tier, and manual-payment billing with email support.
For a shopper, a fashion influencer, a small e-commerce store, or a designer iterating on looks, this layer is what the product is. The model under it is an implementation detail.
When to pick FASHN AI direct
If you are an engineering team integrating virtual try-on into a Shopify store, a styling app, a mobile commerce flow, or any larger application, calling fal.ai directly is often simpler than going through a wrapped product. There is no UI to fight, no auth dance, and pricing is per-image with no platform markup.
If you want the integration convenience without operating your own fal.ai account, Drape Try-On’s Pro plan ($39 per month) addresses the same use case with a Bearer-token REST API, commercial licensing, and managed billing.
Pick Drape Try-On if you are a user. Pick FASHN AI direct (or Drape Pro API) if you are a developer building an integration.
Frequently asked questions
Will my Drape result look different from FASHN direct?+
Can I export Drape outputs commercially?+
Is FASHN AI direct cheaper per image?+
Drape Editorial is the in-house research team behind Drape Try-On. We test virtual try-on models against real garment photography weekly and publish what we learn.