I made a tool to track usage and cost for LLM-powered apps

Hacker News

Hey HN, I’m Kyrylo. While integrating LLMs into my work projects, I kept running into the same issue — tracking LLM usage and costs for each user. Every time, I had to build a custom solution from scratch. So, I built a simple analytics tool to handle it. How it works? 1) Add your app & choose an LLM model; 2) Get an API token; 3) Send LLM usage events into our API. The dashboard shows token consumption, costs, and top users. Pay-as-you-go, no subscriptions, and free usage available. I’d love to hear your feedback and ideas for improvement!

Visit product View on Hacker NewsFeb 24, 2025

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

92%92% 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.

TrustMRRLess likely to generate early MRR · Strong signals: apps, users · Missing: mobile apps, ios, personal

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, 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.

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

31%31% 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

17%17% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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