Image and video decision
Glamour
A small technical team can reproduce the core color-analysis and virtual-makeup features using open libraries and the cited prior-art; the main value the vendor retains is App Store distribution, polish, and any proprietary assets.
View on the App Store↗Not priced
No pricing page we fetched carried a figure, so there is nothing to compare against. The build side is still real.
$100one-off32 h to build
$0/mo6 h/mo upkeep
No published price to break even against.
Open-source builds that already do this
Every project below is open source and already does this job today. Fork one, self-host it, or take the parts you need - the build prompt further down assumes an empty file, and this is the shortcut past that. Licences differ; check the one on each card before you ship. All Glamour alternatives, with the arithmetic →
What a replacement has to do
- User uploads or captures a selfie → detect face and isolate skin region → extract dominant skin/feature colors and map to a seasonal palette → render palette and overlay virtual makeup looks for preview → allow saving/sharing of palette and images.
What it still won’t have
- Polished native iOS App Store UX and App Store distribution
- Built-in App Store in-app purchase handling and subscriptions
- Any proprietary datasets or proprietary ML models used for higher-quality color classification
- Polished library of makeup looks and content curated by the vendor
What remains hard
- Brand trust
73 Ratings 4.4
First-year cost
No published price
Glamour does not publish a price we could read, so there is nothing to compare against. What building costs is below.
Money you would actually spend
Time you would spend
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What you would spend
What we assumed
The verdict above measures whether you could build it. This one is only about money.
Runnable build prompt
Build a minimal self-hosted color-analysis web app using React for the frontend, Flask (or FastAPI) for the backend, PostgreSQL for metadata, and AWS S3 for image storage. Core features: 1) camera and image upload UI, 2) server endpoint that runs face detection and facial landmarking (face-api.js or OpenCV + dlib) and returns a skin mask, 3) color extraction service that quantizes masked pixels (k-means or median-cut) and maps results to seasonal palettes via a rule table, 4) virtual makeup overlay engine that applies tint/alpha blends to facial regions, 5) simple user gallery to save/share palettes and images, 6) basic auth and per-user storage. Out of scope: App Store packaging, subscription billing, large-scale analytics, and proprietary model training. Include request validation, error handling, unit tests for color-mapping logic, and end-to-end tests for upload→analysis→preview flow.
How we checked
How the score was reached
- Build verdict base78
- An open-source build was found+5
- 2 cited sources+1
- Evidence score84
The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time - so the same evidence always produces the same number.
How scoring works →Cited sources · 2
Every page the run actually retrieved.
- official productGlamour:Color Analysis&Glow Up - App Store
- open sourcephotonix — photo management server (prior art)
Integrity checks
What held up, and what did not.
