Lotus – Open source pricing and packaging infrastructure

Hacker News

Hi HN! We’re building an open-source pricing & packaging engine for SaaS with a built-in billing system. ( https://github.com/uselotus/lotus ). We strongly believe pricing is the largest untapped growth lever for SaaS, primarily because pricing affects so many critical systems that don’t talk to each other (billing, payments, feature limits, metering, and CRM). We’re building this infrastructure to fix this and enable quick experimentation. Lotus acts as a central repository for all of your pricing plans and utilizes your payment gateway, to manage usage-based, per-seat, and custom enterprise pricing. We’re excited to open-source this because we want to enable developers to build their custom pricing and integration edge cases on top of this base. We’ve launched this repo under an MIT license so any developer can use the tool. Give it a spin for us at either: * test our cloud version at ( https://demo.uselotus.io ) * self-host here ( https://github.com/uselotus/lotus ) and let us know what you think. All feedback is appreciated! If the project is especially relevant to you, follow us and we’ll keep you updated when we’ve fully published all our beta features.

Visit product View on Hacker NewsNov 6, 2022

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

77%77% 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: excited, open source, io · Missing: https docs, just released, exist

68%68% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

43%43% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

22%22% 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.

Correct prediction on native model

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