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Drinqly
Drinqly — a journal for everything you drink
About Drinqly
Log every drink, build a Drink Story, see what friends are pouring, and find what to order next. The bar, or stone-cold sober.
- Who it is for
- Intentional drinkers age 25-45 in cities who care about taste and want to remember drinks and nights out, not just track calories or check in for badges.
- Problem it solves
- Existing drink-logging apps box people into one category. Untappd is built for beer, Vivino is built for wine and selling it to you. If your night out spans a cocktail, a glass of natural wine, and a can of something new, there's nowhere to put it all. And even within those apps, logging feels transactional: a rating, a checkbox, done. There's no good way to actually remember why a drink mattered, what the night felt like, or notice your own taste evolving over time.
- The solution
- Drinqly lets you log any drink, in any category, with the same care. A Quick Sip captures something fast; a Full Check-In pairs the drink with a photo from the night out, so the entry reads like a memory instead of a database row. Over time those check-ins build a Drink Story, a personal timeline that surfaces patterns like which cocktails you rate highest or which spots you keep returning to. AI menu scanning speeds up logging at the bar, Google Places ties check-ins to real venues, and a social feed, wishlists, and challenges add a layer for sharing with friends.
- What makes it unique
- It's drink-agnostic by design, so it doesn't force you into beer or wine the way competitors do. And it's built around memory, not consumption: the drink photo plus the night-out photo is the core mechanic, which is closer to Letterboxd than to a calorie or check-in tracker. It's also solo-built and shipped fast, which shows up as a tighter, more opinionated product than the category incumbents.
Starting price: 4.99
Features
Keep track of every drink
Add images of your drinks and the menu
See what others are sipping
Details
- Platforms
- iOS
- Web
- Pricing
- Freemium
- Subscription
- Built for
Intentional drinkers age 25-45 in cities who care about taste and want to remember drinks and nights out, not just track calories or check in for badges.
- Building since
2026
- Growth channels
- Product Hunt
- SEO
Drinqly app profile showing Hailei s stats and Negroni and Old Fashioned drinks
1 / 3Website performance
Recorded mobile lab measurements and finalized weekly standings from IndieTools Speed.
74GoodMobile Lighthouse
Measured · UTC
- Load time
- 713 ms
- FCP
- 817 ms
- LCP
- 9.77 s
- CLS
- 0.002
- TTFB
- 14 ms
- TBT
- 0 ms
- Speed index
- 3.21 s
These stored lab observations describe controlled test runs. They are not real-user Core Web Vitals or a guarantee of search rankings. Weekly standings below remain attached to their finalized edition.
- Overall rank
- Not finalized
- Best overall rank
- Not finalized
- 0 top 10 finishes
- Best category rank
- Not finalized
- 0 category wins
- Latest change
- -1 score
- No finalized score yet
Method: psi-mobile-two-sample-mean-v1 · 2 samples per reading. Only readings with the same recorded method and sample count share a chart. Lab results can vary between runs.
Website performance history
Inspect every successful mobile Lighthouse lab measurement by metric and date range.
Range
Metric
Latest score
74 / 100
Higher is better
34 measurements
Use the left and right arrow keys to inspect measurements. Use Home or End to jump to the first or last measurement. Press Escape to dismiss the chart tooltip.
Oct 9, 2026Nov 7, 2026Domain Rating
Domain Rating by Ahrefs- DR4 (primary model)
- 0
First DR4 reading
- DR3
- 2
No change since 6 Nov
Model difference−2. DR4 is 2 points lower than DR3.
Measured · drinqly.io
What do DR4, DR3 and the model difference mean?
- DR4:
- Ahrefs' newer model. It gives more weight to links from sites with organic search traffic and rates a subdomain on its own links.
- DR3:
- The previous model, shown in the Ahrefs app until 1 December 2026.
- Model difference:
- DR4 minus DR3 for the same domain. The models calculate differently, so this is not growth or a drop; the “since” lines compare each model with itself.
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