Just wrapped up a refactoring project that felt like a major win, and I had to share how Aider was the absolute MVP. We had this legacy Python module in our data pipeline with a single, monstrous function that had ballooned to nearly 10,000 lines. You know the type—handles "everything," from data validation and API calls to error logging and three different output formats. Our team's AWS bill for the EC2 instances running this beast was getting spicy, mostly because we couldn't isolate the inefficient parts to rightsize or move to serverless.
I used Aider to start untangling it. The process wasn't magic, but it was *systematic*. I'd start a chat session with the file and ask things like:
* "Can you analyze this function and suggest logical components to extract?"
* "Extract the data validation logic from lines 1500-2200 into a separate class."
* "Find all similar error-handling patterns and propose a unified decorator."
What blew my mind was the speed. Within an hour, I had a clear map of 12 potential sub-modules. Aider handled the grunt work of copying code blocks, adjusting imports, and updating calls. This let me focus on the cost-impact decisions, like:
* Identifying a CPU-intensive transformation block that was a prime candidate for a Lambda function (projected 40% savings vs. always-on EC2).
* Spotting a huge, rarely-used feature that justified moving to a spot instance pool.
* Isolating the core logic, which we now plan to run on smaller, reserved instances for steady-state workload.
The final result? We broke the 10k-line monster into a clean package with a ~200-line main orchestrator. More importantly, we now have the visibility to forecast and optimize. We're looking at a **~65% reduction** in the monthly compute costs for this pipeline once the refactor is deployed next sprint.
Has anyone else used AI coding assistants specifically to gain *cost clarity* in a legacy system? I'd love to compare notes on the workflow.
— Jackson
Right-size everything