Health, home and travel decision
Prettier
A technical user can build a narrow but useful version (selfie upload + third-party vision + rule-based recommendations) within a week, but replicating a polished commercial product, proprietary models/datasets, and market trust is not realistic without the vendor's resources.
Visit website↗Not priced
No pricing page we fetched carried a figure, so there is nothing to compare against. The build side is still real.
$50one-off24 h to build
$40/mo3 h/mo upkeep
No published price to break even against.
No open-source build does this yet
Nothing published replaces this one, so a replacement starts from an empty file. Here is what it would have to cover.
What a replacement has to do
- Upload selfie → detect face, skin tone, and features → infer color season and makeup recommendations → render personalized guidance and example looks.
What it still won’t have
- Polished UX, cross-platform mobile apps and brand polish.
- Any proprietary, trained models or proprietary datasets the vendor may use for higher-quality predictions.
- Integrated marketing, user base, and any commercial moderation/analytics the vendor operates.
What remains hard
- Product polish and ongoing maintenance
First-year cost
No published price
Prettier 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
—
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 Prettier-like web app using React for the frontend, a small Node.js + Express backend, PostgreSQL for profiles, and S3-compatible object storage. Core features in scope: signed selfie upload, server-side call to a third-party vision/face-analysis API (or a self-hosted open model) to extract skin tone, undertone, and facial landmarks; deterministic mapping rules that convert analysis outputs to a color-season and a short makeup routine; API endpoints to save/load user profiles; and a responsive UI that displays recommended shades and step-by-step instructions. Out of scope: training custom ML models, mobile native apps, multi-tenant billing, and advanced analytics. Include input validation, error handling for failed image analysis, retries for external API calls, and automated unit and integration tests for the backend endpoints.
How we checked
How the score was reached
- Partly verdict base52
- 3 cited sources+3
- Evidence score55
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 · 3
Every page the run actually retrieved.
- official productPrettier - Glow Up Instantly (home)
- official docsPrettier features
- official pricingPrettier pricing
Integrity checks
What held up, and what did not.
