Lotus – open-source pricing engine

Hacker News

Hey HN! We discovered that no one innovates on pricing because every billing software is inflexible. Excited to share our progress with Lotus entering beta (full MIT license, https://github.com/uselotus/lotus ). New features: * backtest and run analysis comparing multiple plans * version pricing plans like you version code * Meter and aggregate usage flexibly with custom SQL * billing for usage-based pricing, hybrid, bespoke contracts * generate invoice pdfs or integrate with Stripe, Braintree, Netsuite, Salesforce * webhooks to send alerts when usage thresholds are high or build robust invoicing integrations * analyze margin and cost per customer All feedback is appreciated! If the project is especially relevant to you, reach out on our website. Check out our brand new self-serve demo here ( https://demo.uselotus.io ). Or self-host here ( https://github.com/uselotus/lotus ) and let us know what you think.

Visit product View on Hacker NewsMar 24, 2023

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

90%90% 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, 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

48%48% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

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

30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: margin · 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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