Everyone's raving about the time saved. But saved from what? I've seen too many "productivity" tools that just shift the effort elsewhere.
Track the delta between manual implementation time and your actual Aider sessions. Include the time spent on:
- Prompt engineering and re-explaining the task
- Reviewing and fixing the generated code
- Context switching when it goes off the rails
Compare that to your old workflow's total cycle time. Bet the "savings" shrink when you account for the new overhead.
Your stack is too complicated.
I moderate discussions on AI dev tools and ran Aider in a ~50 engineer SaaS shop (Python/JS/Go stack) for about six months to assess its workflow impact.
**Measurement Method**: Track elapsed clock time from task start to "done, committed, and passing tests." Compare your historical average for similar tasks to your Aider-assisted sessions. The delta is your net gain or loss.
**Major Time Sink - Context Reacquisition**: The biggest hidden cost is the time spent re-prompting after you've been pulled into another chat or tab. Aider doesn't hold state perfectly across interruptions. I'd lose 2-5 minutes per session re-establishing context.
**Review Overhead Is Real**: You must review every change line-by-line. For trivial syntax fixes, this often takes longer than typing it. For larger refactors, the 20-30% of generated code that needs manual correction can eat up the savings if you're not careful.
**Where The Savings Actually Are**: It wins on boilerplate generation and structured refactoring. Creating a new API endpoint with tests was consistently 40-50% faster. Renaming a variable across a 15-file microservice went from a 15-minute chore to a 2-minute verified change.
My pick is Aider for greenfield component work or widespread syntactic refactors in a familiar codebase. If your tasks are mostly bug fixes in legacy systems, the context rebuilding kills efficiency. Tell us your primary use case (new features vs. maintenance) and team's average familiarity with the codebase.
—AF