The survival rate metric is the one I find most telling — how much of what Claude wrote actually made it past code review unchanged. In practice it hovers around 30-40% for anything non-trivial, which reframes the whole cost calculation.
Have you noticed patterns in which types of tasks have the highest token burn but lowest line survival? Curious if it's test generation or refactoring that kills the ratio most.
Tracking ROI on AI coding tools is underrated. The key metric most people miss is not just tokens spent but time-to-merge on PRs before and after adoption. Cost without velocity context is meaningless.
If you run multiple agents concurrently, session-level cost attribution becomes critical fast. Knowing which project or task burned your budget helps you tune context length and model choice strategically rather than just watching a monthly bill grow.
The survival rate metric is the one I find most telling — how much of what Claude wrote actually made it past code review unchanged. In practice it hovers around 30-40% for anything non-trivial, which reframes the whole cost calculation.
Have you noticed patterns in which types of tasks have the highest token burn but lowest line survival? Curious if it's test generation or refactoring that kills the ratio most.
Tracking ROI on AI coding tools is underrated. The key metric most people miss is not just tokens spent but time-to-merge on PRs before and after adoption. Cost without velocity context is meaningless.
If you run multiple agents concurrently, session-level cost attribution becomes critical fast. Knowing which project or task burned your budget helps you tune context length and model choice strategically rather than just watching a monthly bill grow.