Trackion
AI spend visibility across models, workflows & teams.
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Trackion - AI spend visibility across models, workflows & teams.

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Trackion gives AI-native teams complete visibility into where every AI dollar goes. Most AI companies know their monthly API bill but not which features, models or workflows are driving it. Trackion connects the dots, helping engineering teams monitor AI spend across providers, identify hidden cost drivers, detect unusual usage, forecast future costs and make confident scaling decisions. Built for startups using OpenAI, Claude, Gemini and other leading AI models.

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Ciao Product Hunt 👋 Joshua here, founder of Trackion. We built Trackion after noticing the same pattern repeatedly while speaking with AI startups and agencies: Most teams could see their total AI bill… but had almost no visibility into: * which workflow caused it * which feature scaled * which clients were driving usage * or where margins were quietly disappearing AI costs don’t usually spike evenly. One automation, one feature, or one client can suddenly dominate infrastructure spend without teams realising until later. Trackion was built to give AI teams operational visibility across: * OpenAI * Claude * Gemini * AI workflows * token usage * infrastructure costs Would genuinely love feedback from founders, CTOs, and AI builders here. Happy to answer any questions throughout the launch 🙌

How does the forecasting actually work in practice, does it just extrapolate from past usage or does it factor in things like planned model switches or new feature rollouts?

How does this handle multi-provider attribution when a single feature calls several models in the same workflow, like OpenAI for embeddings and Claude for the final response?

Finally something that shows which feature is actually eating my OpenAI budget. Set it up in about ten minutes and spotted a runaway workflow I had no idea about.