Health, home and travel decision
HedgePal
A basic version (photo upload + third-party vision + a seeded nutrition DB) is realistic for one developer in ~40 hours, but reproducing Hedgepal's claimed scale, proprietary food database, polished mobile UX and telehealth features requires more resources.
Visit website↗Built by Gaurav Sapkota (Garry), who ships 3 products in this index
Not priced
No pricing page we fetched carried a figure, so there is nothing to compare against. The build side is still real.
$100one-off40 h to build
$50/mo3 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 HedgePal alternatives, with the arithmetic →
What a replacement has to do
- User takes or uploads a photo -> send image to a vision model/API to identify foods and estimate portions -> map identified items to nutrition database entries to compute calories/macros -> store meal log and update daily goals/streaks -> surface charts and suggestions to user.
What it still won’t have
- Hedgepal's claimed "database of over 1 million foods" and the coverage/curation that brings
- Polished mobile UX, user-tested heuristics, and in-app telehealth booking
- Proprietary AI model tuning and any model improvements trained on internal data
- Any built-in end-to-end encrypted data handling unless explicitly implemented
What remains hard
- Product polish and ongoing maintenance
First-year cost
No published price
HedgePal 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 AI calorie-tracker web app using React (mobile-first) + Node.js/Express + Postgres. Core features in scope: image upload UI, server-side image resize, integration with a third-party vision API (e.g., Google/Cloud Vision or an open hosted model) to return food labels, a seeded nutrition lookup table (import USDA or other public dataset) to map labels to calories/macros, portion estimation heuristics, user auth, storing meal logs and weight history in Postgres, and simple charts for daily calories/macros and streaks. Out of scope: training custom vision models, telehealth booking, family/team plans, and a 1M-entry curated commercial food database. Require error handling for failed image calls, invalid nutrition lookups, and DB failures; include unit tests for the nutrition API and an end-to-end test for the scan->log flow.
How we checked
How the score was reached
- Partly verdict base52
- An open-source build was found+5
- 2 cited sources+1
- 3/3 assessment runs agreed+4
- Evidence score62
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 productHedgepal - AI Calorie Counter
- open sourcesimonoppowa/OpenNutriTracker
Integrity checks
What held up, and what did not.
