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Frost AI – Stop losing money on AI agents

Hacker News

Frost AI – Stop losing money on AI agents

Hi HN, Florent here, one of the founders of Frost AI. Before that, we tried to build an AI Agent for a friends business. We realized we'd have to build a whole system for usage tracking, per-user cost analysis, subscription management, and a customer billing portal. We had to spend more time on boilerplate billing code than on the AI itself. A single user in a weird loop could rack up a massive bill before we even noticed. So, we started building Frost AI. Here’s what it does: * Monetization SDK: Add our SDK with just a few lines of code and start charging users based on usage, subscription tiers or credits. * Real-time Profitability Tracking: This is the core. We automatically track your underlying model costs (OpenAI, Anthropic etc.) against the revenue from each user, so you know exactly if a user is profitable. * Instant Unprofitability Alerts: The moment a user's session starts costing you more than they're paying, you get an alert to take action. No more surprise bills. * White-Label Customer Dashboard: We give you a pre-built portal where your customers can manage their subscription, see usage, and track their own ROI. Our goal is simple: let developers focus on building great AI, not on rebuilding a billing system from scratch. We're just getting started and would be grateful for any feedback. Get Early Access: Website: https://www.frostai.dev Feel free to get in touch: https://x.com/florentmsl

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor
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 · Strong signals: started · Missing: supports, reddit linkedin, podcasting
85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
45%45% 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, profitable · Missing: mobile apps, ios, personal
44%44% 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, profitable · Missing: arr, mrr, saas
35%35% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
25%25% 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.

Correct prediction on native model

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