See where your AI spend is actually going.
The vendor invoice tells you what you spent, not why. Engineers use Opus when Sonnet would do. Conversations sit open for days, re-paying for the same context every turn. Slides get regenerated five times for one-word edits. The waste lives in the request, not the route, and your vendor has no commercial reason to surface it.
Flowstate sits between user and model on every device, so the why becomes a chart.
The waste is real. The research is published.
We’re smart, but others are smarter. The economics of agentic AI are now being studied empirically — and the numbers are wild.
“Runs on the same task can differ by up to 30x in total tokens.” Same prompt, same engineer, same model — thirty times the cost.
Bai et al., “How Do AI Agents Spend Your Money?” arXiv 2026 →“Kimi-K2 and Claude-Sonnet-4.5, on average, consume over 1.5 million more tokens than GPT-5” on identical SWE-bench tasks. Model choice is a cost decision.
Bai et al., arXiv 2026 →“Higher token usage does not translate into higher accuracy; instead, accuracy often peaks at intermediate cost and saturates at higher costs.” The expensive runs aren’t the smart runs.
Bai et al., arXiv 2026 →The vendor knows. The vendor has no commercial reason to tell you. Flowstate sees the same data they do — on your devices, in your seat, attributed to your projects.
The modules that solve this
Two products. One ledger. Visibility you can reconcile back to the P&L.
See it on your data.
Book a demo and we’ll show you which prompts, which projects and which people are driving your AI line item — on your own data, inside a week.