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Credyt – Real-time billing, built for AI

Hacker News

Credyt – Real-time billing, built for AI

Hey HN! I'm the founder of Credyt ( https://credyt.ai ). If you're running an AI product that burns $0.50+ per interaction, you need to bill customers as usage happens, not at the end of the month. Otherwise you're a bank for your customers, and you'll run out of money before they do. Why invoice-based billing doesn't work for AI: Most usage-based billing tools (Stripe Billing, Orb, etc.) were built for SaaS. They meter usage all month, then send an invoice. That works fine when your COGS is 5%. It's a disaster when you're paying your AI vendor upfront for every request and your margins are 15-30%. If a power user runs up $10K in costs and churns or their card declines at month-end, you've just eaten $10K. We've seen multiple startups nearly run out of runway because of this. "Just build a wallet in Postgres" – harder than it sounds: Everyone starts there. Track a balance, decrement on usage, done. Then you hit race conditions when usage spikes, auto-top-up logic that needs to be bulletproof, credit grants with expiry dates and priority rules, pricing versioning (customer X is on v1 rates, customer Y on v2, customer Z has a custom deal), profitability analysis (what did this customer cost us vs. what we charged them?), refunds, disputes, partial credits, reserves... Three months later you've hacked together a billing system instead of building your product. What Credyt does: It's a wallet-native billing engine. Your customers get a balance (in USD, credits, tokens, minutes, whatever you want), you send usage events as they happen, and Credyt handles the debiting, auto-top-ups via Stripe, and gives you an embeddable billing portal. We match against your pricing rules (which you can version and test before publishing), debit the wallet atomically, trigger top-ups via Stripe when they hit their threshold, and give you a hosted billing portal so you're not building balance UIs. Unlike tools that only track revenue, Credyt lets you log both what you charged and what it cost you. So you can see margins per customer, per product, per pricing tier—in real-time. Most founders are shocked when they realize their most active users are unprofitable. Docs: https://docs.credyt.ai Quickstart video: https://www.youtube.com/watch?v=48XJlId7098 Pricing: Free to start, then $1/month per active wallet. No processing markup—Stripe fees pass through directly. We're still early stage and genuinely want feedback from people in the trenches: What would stop you from ripping out your current system for this? What's missing that would make this production-ready for you? If you tried to build wallets yourself, where did it break?

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, stripe · Missing: mac, agents, macos
91%91% 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
91%91% predicted probability of success on Indie Hackers, 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
65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: video, month, users · Missing: mobile apps, ios, personal
50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: revenue, profit, saas · Missing: arr, mrr, ttm
36%36% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host, users · Missing: plus, platform, intuitive
23%23% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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