AI Cost per Workflow Run
Industry benchmarks for ai cost per workflow run in 2026, segmented by team size.
Metric: Median AI spend per run (USD)
| Segment | Low (P25) | Median (P50) | High (P75) |
|---|---|---|---|
| Solo / Startup | 3 | 3 | 14 |
| Small Team (10–50) | 9 | 15 | 38 |
| Mid-Market (50–500) | 15 | 45 | 110 |
| Enterprise (500+) | 70 | 170 | 420 |
About this benchmark
This benchmark covers median ai spend per run across teams running automation in production. The data is aggregated across 1,200+ workflows and segmented by team size to give you a realistic comparison.
How to read the table
The Low (P25) column shows the value where the bottom quartile of teams sits. Median (P50) is the middle of the distribution. High (P75) shows the value where the top-performing quartile lands.
Teams in the High column generally invested more in workflow design, observability, and error handling. The bottom quartile typically hand-built workflows without much testing or version control.
What drives the spread
For ai operations specifically, the largest source of variance is workflow design discipline. Teams that treat workflows as code — with versioning, tests, and observability — end up in the High column reliably. Teams that wing it usually end up in the Low column.
Improving your number
If your team is in the bottom quartile, the highest-leverage move is usually adding observability so you can measure the right thing in the first place. Most teams discover that their actual numbers are different from their assumptions once they instrument properly.