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Flexprice: Open-Source Usage-Based Billing Toolkit

Hacker News

Flexprice: Open-Source Usage-Based Billing Toolkit

Hey HN, I’am Koshima, co-founder of Flexprice ( https://flexprice.io/ ). Flexprice is an open source monetization platform for AI & Agentic companies. You can build usage-based, credit-based, or hybrid pricing models with full control. Usage-based billing works on paper, but breaks fast in production: (1) High-volume data (e.g., 10k API calls/sec from AI inferences) breaks naive pipelines, dropping events and causing underbilling or customer complaints. (2) Backfilling missing events or retroactive discounts screws up invoices, forcing 3 AM log stitching to avoid revenue leaks. (3) Prepaid credits and trials are a mess because it requires you to handle expirations, partial deductions, and priority rules. (4) Product teams want real-time, per-customer usage reports (e.g., “How many tokens did user X consume?”), but querying raw events requires slow, custom SQL hacks. (5) Webhook integrations for invoicing or CRMs fail on duplicates, out-of-order events, or time zone bugs, risking customer disputes. (6) What works for 100 users fails at 10,000 because rate limits, and idempotency leads to urgent rewrites. This week, we’re doing a 5-day launch week, where we’re shipping a new set of billing features every day. Today’s launch includes: (1) Fire-and-forget event ingestion with SDKs (Python, JS, Go) and auto retry handling (2) Real-time event debugger for tracking ingestion status, filtering payloads, and inspecting failures (3) Usage analytics API providing billing-grade summaries with filters, groupings, and time-bucketed results (4) Prepaid credit system supporting trial credits, expirations, and configurable priority on deduction (5) Webhook system to automate billing workflows like invoicing or subscription changes We’re shipping new features daily. It’s early, actively evolving, and contributions are welcome. If you’ve dealt with usage-based billing, broken credit logic, or event ingestion at scale, your feedback would help. You can read the details of the launch at https://flexprice.io/blog/the-developer-toolkit . You can clone the project and explore the repo at https://github.com/flexprice , and demo at https://www.youtube.com/watch?v=SFARthC7JXc Would love to hear your technical critiques, edge case concerns, or how you’ve handled billing infrastructure in your own stack.

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, model, agentic · 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.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, pipe · Missing: https docs, excited, just released
66%66% 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: users · Missing: mobile apps, ios, personal
33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users, calls · Missing: plus, intuitive, reviews
25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: revenue, subscription, active · Missing: arr, mrr, profit
22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: paid · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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