BLOG

What is ONNYON?

August 16, 2026

ONNYON is a cycling app that helps you decide what to wear before every ride.

It gives you one place to organize your cycling wardrobe, understand current and upcoming cycling conditions, and plan the ride ahead, while removing as much guesswork as possible.

It's free to use, works on iOS and Android, and runs entirely on your phone. No account, no email, no tracking.

ONNYON home screen
What to wear

ONNYON provides a clear outfit recommendation built around the cycling clothes you actually own.

It looks at three things together: the weather and cycling conditions for your location and route, the items you have in your wardrobe, and your personal preferences.

No more generic advice, just personalized guidance to help you decide what to wear before each ride.

Weather alone does not tell you what a ride will actually feel like.

ONNYON combines the forecast with cycling-specific conditions such as wind exposure, road surface, drying trends, spray risk and current warnings.

Instead of only showing temperature, wind and rain probability, it helps you understand what those conditions could mean once you are out on the bike.

Weather
Routes

Conditions can change significantly from the start of a ride to the highest or farthest point.

ONNYON looks at the route ahead to show how temperature, wind, rain and road conditions may change along the way.

It then helps you understand what to wear from the start and what may be worth carrying for later. Plan your clothing around the whole route!

Add cycling clothes you own to your Inventory, and let ONNYON give you recommendations before your next ride.

Then, in Outfit you can combine your inventory into complete looks, trying out different combinations based on color or brand for example.

Lastly, Brands gives you a dedicated place to explore products beyond your wardrobe as well as receive recommendations and deals of the brands you love.

Wardrobe
Ride log

Keep a clear record of your rides.

Import activities from Strava or log them manually, and indicate the outfit you wore alongside feedback of how it felt.