Trackion
Understand where every AI dollar goes.
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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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Maker
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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 🙌

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.

Maker

 Thanks Gülşah and awesome to hear it spotted a runaway workflow! That's exactly the type of outcome I was hoping for. It's surprising how easy it is for a single workflow to quietly drive up AI costs without anyone noticing. Really appreciate you taking the time to try it and share your thoughts.

Love how cleanly it breaks down spend per feature instead of just dumping an API invoice. That level of attribution is exactly what I've been piecing together in spreadsheets for months.

Maker

  hey love hearing that Onur. That was actually one of the biggest motivations behind building Trackion. API invoices tell you what you spent, but not why you spent it. Being able to break costs down by feature, agent or customer is where the real value starts. Thanks for checking it out, appreciate it.

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?

Maker

 Thanks for the question Gözde. Right now forecasting is primarily based on historical usage patterns and trends. Longer term, I want it to become much smarter by factoring in things like expected growth, model changes, new feature rollouts and budget scenarios so teams can plan ahead instead of just reacting. Really appreciate the question.

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?

Maker

 Hey Cengiz great question.

The goal is for Trackion to treat an entire AI workflow as a single transaction, while still tracking every individual model call underneath it.

So in your example, if a feature uses OpenAI for embeddings, Claude for generation and another model for reranking, each request is logged separately with its own cost, latency and provider, but they're all linked back to the same feature, workflow or agent. That way you can see both the total cost of the workflow and exactly where it's being spent.

Longer term, I also want to surface insights like which step is driving the majority of the cost and where there are opportunities to optimise or switch models.

Really appreciate the question.