Almost every app built on generative AI runs on some kind of credit. It can feel a little abstract at first — you tap a button, a number goes down, and a new image appears. So what actually happened? This is a plain-English guide to what credits are, why they exist, and a few habits that help you spend them well instead of burning through them by accident.
What a credit actually is
Think of a credit as a token that pays for one unit of work the app does on your behalf. With a virtual try-on app, that unit of work is a generation: the app takes your photo and a garment, hands both to an AI model, and the model produces a fresh, realistic image of you wearing that item. Generating an image takes real computing power on a server somewhere, and that compute costs money. A credit is simply the way an app meters that cost so it can keep the lights on without charging you per second.
Why apps use credits instead of “unlimited”
“Unlimited” sounds nicer, but it rarely is. Because each generation has a genuine cost behind it, a truly unlimited model either has to be heavily throttled or priced so high that casual users subsidise the heaviest ones. Credits keep things fair: you pay roughly in proportion to how much you use. They also make the value visible. When you can see what a try-on “costs,” you naturally put a little more thought into the photo and the garment you pick — which, conveniently, tends to give you better results too.
When a credit gets used (and when it doesn't)
As a rule of thumb, a credit is spent when the app does the expensive part: creating a new AI image. Browsing a catalog, scrolling through brands, opening a product page, or re-viewing a look you already generated generally don't cost anything — those are just normal app screens. The spend happens at the moment of generation, when you ask the model to produce a new preview of you in a specific item.
The exact details — how many credits a generation uses, whether certain features cost more, and how often credits refresh — can change over time and differ between plans. So rather than trust a number you read in a blog post, always check the current details inside the app, where they're shown clearly and kept up to date.
Start with a great base photo
The single biggest factor in how far your credits go is the photo you start from. A good base photo gives the AI clear information to work with, so the first result is usually the one you keep — no need to spend more credits re-rolling a bad one. A weak photo, on the other hand, makes the model guess, and guesses cost retries.
What a strong base photo looks like:
- Even, natural light — daylight near a window beats a dim room or harsh overhead bulbs.
- Facing the camera in a relaxed, straight-on pose, with your torso fully in frame.
- A plain, uncluttered backgroundso you stand out clearly from what's behind you.
- Fitted, simple clothing in the photo, so the app can swap garments cleanly instead of fighting baggy layers.
- No heavy filters — the AI matches the lighting it sees, so a filtered photo produces a filtered-looking result.
Plan what you want to try
A little intention goes a long way. Before you start tapping, it helps to have a loose shortlist in mind: the three jackets you can't decide between, the colours you're curious whether you can pull off, the one bold piece you'd never try in a store. Working through a small, deliberate list means almost every generation answers a real question, rather than spending credits on things you were never going to buy.
It also helps to compare like with like. If you're deciding between two coats, try both on the same base photo. That way the only thing changing is the garment, and the comparison is honest. Jumping between different photos and different items at the same time uses more generations and makes it harder to tell what actually looks good on you.
Keep the looks you love
Once a generation lands, it's yours to keep — so treat the results like a little lookbook. Saving the previews you like means you can come back to compare outfits side by side later without regenerating them, which would spend credits all over again. The best workflow is to generate thoughtfully, save the keepers, and make your decision from the images you've already collected.
Be honest about what try-on can tell you
One last thing worth spending credits wisely on: know what a preview is for. Virtual try-on is a visualization tool, not a measurement tool. It does a brilliant job of showing how a colour sits against your skin, how a length works with your frame, or whether a silhouette suits you — the questions a size chart can never answer. What it won't do is tell you whether a medium will pinch at the shoulders. For that, still check the brand's size guide. Spending your generations on the “will this suit me?” questions is exactly where try-on earns its keep.
Credits in TRYSHOP
In TRYSHOP, credits power the try-on generations that put real catalog items from top brands onto your own photo. Everything above applies: start from a clean, well-lit photo, plan the pieces you're genuinely weighing, and save the looks you love so you can compare them without starting over. For the current details on how many credits you have and how they refresh, check inside the app — that's always the most accurate, up-to-date place to look.
