Portcall – open-source billing engine for modern SaaS

Hacker News

Billing should be simple, composable, and flexible. But most tools are either too limited or too heavy. We wanted something in the middle, and made it open source. Portcall gives your SaaS: - Self-serve PLG checkout (payment provider agnostic) - Entitlements as a first-class concept - Usage metering built into the model - Automated invoicing - A unified product/pricing system that evolves with you as you grow - And a lot more to come Curious where billing has bitten you and what you'd want from a modern OSS billing engine. Repo: https://github.com/useportcall/portcall Demo: https://youtu.be/ZJ2sI-VnNiA?si=bVn4xskIcSLKg6hE

Visit product View on Hacker NewsNov 24, 2025

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

71%71% 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: open source, ide, io · Missing: https docs, excited, just released

55%55% 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

46%46% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

42%42% predicted probability of success on TrustMRR, 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: saas · Missing: arr, mrr, revenue

26%26% 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.

Incorrect prediction on native model

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