Ti

Time Travel for Billing Periods

Hacker News

Time Travel for Billing Periods

Hi HN, Lago (YC S21 = Open-source metering and usage-based billing) founder here. Billing cycles are often a source of engineering complexity and confusion, so we made billing periods flexible in Lago: - If you want to migrate existing customers to Lago, you can set a billing period in the past. - If you just signed a new contract that will start in two weeks, you can set a start date in the future Other elements of flexibility: - Switch from 'calendar' to 'anniversary billing periods' within Lago - Assign multiple plans to a customer, and meter and charge usage separately (e.g., if your pricing looks like Heroku, Shopify or Webflow, for instance) Documentation: https://doc.getlago.com/docs/guide/plans/subscription#subscription-date Github: https://github.com/getlago/lago

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, open · Missing: mac, agents, macos
75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Strong signals: para · Missing: supports, reddit linkedin, podcasting
42%42% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription, shopify · Missing: arr, mrr, revenue
23%23% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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