Just finished a big round of testing on our CI/CD pipelines. I wanted to see which AI assistant could best handle script debugging and pipeline-as-code suggestions without breaking the bank.
I tested six popular assistants on the same set of tasks: fixing a flaky GitHub Actions workflow, optimizing a Docker build, and writing a secure secret-handling script. Tracked everything—cost per task, time to correct solution, and accuracy (did it actually run?).
Here's what I found. The open-source/local option was shockingly fast and free, but stumbled on complex security contexts. The high-cost leader was, unsurprisingly, most accurate for obscure errors. The real surprise was a mid-tier model that nailed all the pipeline-as-code tasks with near-perfect accuracy, making it my new go-to for GitOps work.
Full results are in the table linked in the title. Curious if others have seen similar trade-offs between cost and accuracy for automation tasks? ->
Automate everything.