StripeMeter – Open-Source Usage Metering for Stripe Billing

Hacker News

We built StripeMeter, an open-source usage metering platform that plugs directly into Stripe. It solves the classic SaaS pain of “why is my bill higher than expected?” by giving both developers and customers real-time usage tracking with live cost projections. Why it matters - Transparency: Customers see exactly what Stripe will bill them (within 0.5% parity). - Exactly-once guarantee: No double billing, ever. - Fast & scalable: Sub-minute freshness with Redis + Postgres counters. We’d love feedback from SaaS builders, especially if you’ve struggled with Stripe’s metered billing. Does this solve a real pain for you? What would you need before trusting it in production?

Visit product View on Hacker NewsAug 29, 2025

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Did not reach leaderboard

Launch Intel predictions

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

96%96% 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.

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

78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

52%52% 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 · 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: platform, builder · Missing: plus, intuitive, reviews

29%29% 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

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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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