Open-source revenue recognition and analytics for Stripe and Metronome

Hacker News

Hi HN, I’m Tanin. For about four years, I worked on the Revenue Recognition and Analytics teams at Stripe. Today, I'm open-sourcing Book of Revenue, a self-hosted revenue engine and drop-in alternative to Stripe Revenue Recognition (with optional support for Metronome). ### The Problem: Most founders track MRR or cash collected in Stripe and assume that reflects revenue. But cash collected isn't revenue, and MRR isn't an accounting metric. Once you hit scale, raise institutional capital, or prep for an audit, ASC 606 / IFRS 15 rules kick in: - Annual upfront subscriptions must be amortized monthly as deferred revenue. - Mid-cycle upgrades, prorations, usage overages, refunds, and disputes quickly turn into messy manual spreadsheets. - Finance and engineering end up arguing because Stripe dashboard numbers never match QuickBooks or NetSuite during month-end close. ### What Book of Revenue Does: - Automated Revenue Recognition: Computes GAAP/IFRS-compliant revenue schedules from your Stripe billing and payment events out of the box. - Unified Financial Truth: Aligns founder analytics with your accountant’s ledger so both sides look at the exact same numbers. - Full Data Ownership: Self-hosted with no vendor lock-in. You retain complete custody of your customer financial records and intermediate data models. ### Looking for Early Users: I’m looking for early design partners—particularly companies processing over $100k in annual volume who dread month-end reconciliations. I'm offering free hands-on deployment and a complimentary audit of your current Stripe/Metronome billing architecture. If you’d like to test it out, drop a comment below or email me directly at tanin@bookofrevenue.com. Website: https://bookofrevenue.com GitHub: https://github.com/tanin47/book-of-revenue

Visit product View on Hacker NewsSep 18, 2026

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

72%72% 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: month, monthly, users · Missing: mobile apps, ios, personal

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host, users · 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: mrr, revenue, subscription · Missing: arr, profit, saas

28%28% 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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