Meter/Aggregate your product usage data (no-code, no login, no charge)

Hacker News

If you’re using Stripe for your SaaS product and if you have a usage based component in your pricing - this is for you. Aggregating usage data into a digestible format for Stripe is a monthly chore no one likes to do. - The computations are complex and ripe for errors - It’s repeated manual grunt work - having to aggregate and calculate usage for every customer every month - You’re restricted to the pricing models that your billing systems allows you to have This is what we solved with metering.ai. With metering.ai as a free add-on to your existing billing system, you can meter your product’s usage without any engineering effort. This is how it works: 1. Upload your usage data as a .csv file 2. Connect to your Stripe account 3. Enter your aggregation formula And voila - aggregated usage data for every customer is sent to Stripe and ready to be invoiced. No more manual work. And no more billing errors.

Visit product View on Hacker NewsApr 27, 2023

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

93%93% 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: exist, existing, io · Missing: https docs, excited, just released

51%51% 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 · Missing: mobile apps, ios, personal

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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.

Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue

27%27% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

22%22% predicted probability of success on AppSumo, 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.

Correct prediction on native model

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