Lago: Open-source metering and usage-based billing

Hacker News

At Lago, we strongly believe that billing, whether it's subscription-based, usage-based or hybrid, is still a nightmare for engineers. Many of the existing billing engines are home-made, painful to build and maintain. Billing shouldn't require a dedicated team within your companies, or hours per week of maintenance. It should be easy to track, price and experiment usage. Our vision is to help your company switch to usage-based billing easily. Our open-source project lets you set up any types of pricing in hours, not months. Lago is particularly useful for hybrid, usage-based or per-seat pricing. It's also incredibly flexible for metering. - Lago Open-Source: https://github.com/getlago/lago - Lago Cloud: reach out at https://forms.gle/eT7zgqcvq8dgt94g7 - Documentation: docs.getlago.com - Open Roadmap: https://github.com/getlago/lago/projects/2 Your feedback is our fuel, so don't hesitate to try this yourself or reach out

Visit product View on Hacker NewsNov 7, 2022

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Product HuntOn track for Day 1 leaderboard · Strong signals: open · Missing: mac, agents, macos

85%85% 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, io · Missing: https docs, excited, just released

67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

36%36% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue

20%20% 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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