Olive: Food & Cosmetic Scanner vs AutoSleep: Watch Sleep Tracker
Revenue, downloads, ratings and category context for two iOS apps. Figures are estimates and should be read as ranges, not exact totals.
Olive: Food & Cosmetic ScannerGiga Studios, Inc - Health & Fitness
AutoSleep: Watch Sleep TrackerTantsissa - Health & Fitness| Metric | Olive: Food & Cosmetic Scanner | AutoSleep: Watch Sleep Tracker | Readout |
|---|---|---|---|
| Estimated revenue / mo | $*** - opens with trial | $*** - opens with trial | Olive: Food & Cosmetic Scanner |
| Revenue band | $*** - opens with trial | $*** - opens with trial | Use range + confidence |
| Estimated downloads / mo | $*** - opens with trial | $*** - opens with trial | Olive: Food & Cosmetic Scanner |
| Download band | $*** - opens with trial | $*** - opens with trial | Use range + confidence |
| Rating | 4.81 | 4.71 | Olive: Food & Cosmetic Scanner |
| Rating count | 40k | 61k | AutoSleep: Watch Sleep Tracker |
| Tracked keywords | 1 | — | Olive: Food & Cosmetic Scanner |
| Category | Health & Fitness | Health & Fitness | Same market |
Which app is bigger?
Olive: Food & Cosmetic Scanner and AutoSleep: Watch Sleep Tracker are compared inside Health & Fitness with monthly revenue, downloads, rating, review volume, keyword footprint, category placement and range context. Olive: Food & Cosmetic Scanner leads on monthly downloads in the current readout, and Olive: Food & Cosmetic Scanner wins the revenue readout (the exact figures open with the paid plan), Olive: Food & Cosmetic Scanner wins demand, AutoSleep: Watch Sleep Tracker has deeper review volume, and Olive: Food & Cosmetic Scanner has the larger tracked keyword footprint. The better app depends on the job: choose the revenue leader when you need monetization proof, choose the download leader when reach matters, and choose the higher-rated or deeper-reviewed app when market trust is the constraint. Read both ranges rather than only the midpoint, then open the category hub and related collections to see whether this pair is an outlier or a normal category pattern.