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Autumn – Open-source infra over Stripe

Hacker News

Autumn – Open-source infra over Stripe

Hey HN, I’m Ayush from Autumn ( https://useautumn.com/ ). Autumn is an open source layer over Stripe that decouples pricing and billing logic from your application. We let you efficiently manage pricing plans, feature permissions, and payments, regardless of the pricing model being used. It’s a bit like if Supabase and Stripe had a baby. Typically, you have to write code to handle checkouts, upgrades/downgrades, failed payments, then receive webhooks to provision features, reset usage limits etc. We abstract this into one function call for all payments flows (checkouts, upgrades, downgrades etc), one function to record usage (so we can track usage limits), and a customer state React hook you can access from your frontend (to handle paywalls, display usage data etc). Here’s a demo: https://www.youtube.com/watch?v=SFARthC7JXc Stripe’s great! But there are 2 main reasons people use Autumn over a direct Stripe setup: (1) Billing infra can get complex. After payments, there’s still handling webhooks, permission management, metering, usage resets, and connecting them all to upgrade, downgrade, cancellation and failed payments states. (2) Growing companies iterate on pricing often: raising prices, experimenting with credits or charging for new features, etc. We save you from having to handle usage-based limits (super common in pricing today), rebuilding in-app flows, DB migrations, internal dashboards for custom pricing, and grandfathering users on different pricing. Ripping out billing flows etc, really sucks. With Autumn, you just make pricing changes in our UI and it all auto-updates. We have a shadcn/ui component library that helps with this. Because we support a lot of different pricing models (subscriptions, usage, credits, seat based etc), we have to handle a lot of different scenarios and cases under the hood. We try to keep setup simple while maintaining flexibility of a native integration. Here’s a little snippet of the architecture of our main endpoint: https://useautumn.com/blog/attach Currently, the users who get the most value out of us are founders that need to move fast and keep things flexible, but also new/non-technical devs that are more AI native. You can clone the project and explore the repo, or try it out at https://useautumn.com/ , where it’s free for builders. Our repo is https://github.com/useautumn/autumn , docs are at https://docs.useautumn.com/ and demo at https://www.youtube.com/watch?v=SFARthC7JXc We’d love to hear your feedback and how we could make it better!

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Actual performance

141points
47comments
Made the leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, stripe · Missing: mac, agents, macos
95%95% 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 · Strong signals: ios, efficiently · Missing: supports, reddit linkedin, podcasting
81%81% 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, io · Missing: https docs, excited, just released
58%58% 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: ios, users · Missing: mobile apps, personal, entrepreneurs
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: efficient, builder, users · Missing: plus, platform, intuitive
28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue
18%18% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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