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Splid – Split group bills

Splid – Split group bills

Nicolas Jersch

Finance免费v1.9.1
App Store
评分

4.9

3,638 条评分

星级

★★★★★

最近更新

2026年5月4日

发布日期

2016年5月14日

更新内容

v1.9.1

Thank you so much for your helpful feedback and the superb ratings. This helps me to make Splid better with every new release. New in this version: - A fresh look on iOS 26 - Expenses are now grouped by date - A number of smaller improvements

应用信息

开发者
Nicolas Jersch
分类
Finance
价格
免费
版本
1.9.1
App ID
991473495

简介

Perfect for holidays, housemates or your relationship, Splid helps you to stay on top of your expenses and settle up in an easy, relaxed way. No more fiddling about with change, lost receipts, or disagreements about the balance. Simply enter all your expenses and Splid shows you who owes how much to whom. And the best thing: Splid works on and offline. Create an offline group and get settling up under control within seconds. Or, activate the sync to enter expenses together. It's simple, and there's no sign-up required. Even complicated bills can dealt with quickly and easily with Splid: – Emma paid the supermarket bill but Leo contributed $10? No problem. – Your expenses are in dollars but you want to settle up in euros? Done.  – Hannah had two more drinks than everyone else? Easy-peasy.  All features at a glance: (+) Clean interface that's super easy to use. (+) Share groups online (no sign-up needed). (+) Also works perfectly offline. (+) Download summaries as PDF or Excel* files that are easy to understand. (+) Choose from more than 150 currencies and let Splid automatically convert the amount. (+) Handles even complicated transactions (for example, adding multiple payees or splitting bills unequally). (+) You'll handle as few payments as possible because Splid always finds the easiest way to settle up.  (+) Find out how much your group has spent in total. *Excel export available via in-app purchase.

下载量预测

专业 · 预览

预估总下载量

485K364K728K
保守估计乐观估计

3K

低 / 月

4K

预估 / 月

6K

高 / 月

基于3,638 条评分
假设评分率0.8%
应用年龄122 个月

基于评分数量 ÷ 类别评分率估算,实际下载量误差可达 ±50%,与 Sensor Tower 方法一致。