What does it actually cost to switch AI coding tools?
Engineering managers switch tools based on benchmarks and X hype. Nobody runs the math first. Enter your team details — get engineer-hours, total cost, and break-even timeline.
Vendor benchmarks run on toy problems. Real-world gains are typically 5–20%. Be honest — expected gain is the most important variable.
01 / Context migration
CLAUDE.md, .cursorrules, custom instructions. Every tool has its own format. Migration is manual and scales with months of accumulated customization.
02 / Workflow reconfig
Keybindings, IDE integrations, CI hooks, pre-commit configs, team conventions. Each engineer reconfigures independently.
03 / Retraining
New prompting patterns, new failure modes. Engineers who were expert-level on the old tool start over. Longer usage = deeper habits = higher cost.
04 / Productivity dip
The largest cost and the most ignored. 3–6 weeks where output drops while the team adjusts. This is the valley between tools.
Full methodology, tool complexity scores, and sources → methodology page
One company with 600 engineers switched AI coding tools 3 times in 9 months. Nobody calculated the switching cost before any of them.
The total cost was in the hundreds of thousands. They found out after.
One switch done right beats three switches done fast.
- →Run this calculator before any switch
- →Pilot with 2–3 engineers for 4 weeks first
- →Benchmark on your actual codebase
- →Budget for the productivity dip explicitly
- →Wait 60 days after a tool launches
- →Switch on a benchmark you didn't run yourself
- →Switch because engineers are asking for it
- →Switch before break-even on the last switch
- →Trust vendor productivity claims at face value
- →Roll out to the full team without a pilot
Built by Siddhant Khare — software engineer at Ona, OpenFGA core maintainer (CNCF), author of The Agentic Engineering Guide.
If you're an EM or director making this call for your team, DMs are open. No pitch — just the actual data, IFs, and BUTs.