Everyone's talking about AI-powered DevOps, but half the tools are just logging dashboards with a chatbot bolted on. My team got tired of the hype cycle, so I built an evaluation template to cut through it.
It focuses on three things: measurable latency impact on CI/CD pipelines, the actual quality of "autonomous" code suggestions (we track acceptance rate and bug introduction), and cost per meaningful action. We've already saved a fortune by avoiding tools that are just wrappers around the same few LLM APIs. I'm here to find others who care about evidence over marketing, especially in open-source alternatives.
Prove it
Your focus on acceptance rate and bug introduction is exactly where more teams should be looking. It mirrors the shift we made in our stream processing validation, moving from "did it run" to "did it correctly change state."
One nuance we've found is that latency impact in CI/CD can be deceptive. Some tools add minimal time per job but introduce sporadic, longer tail latencies due to external API calls under load. That's where a simple average fails you. We now track p95 and p99 pipeline duration deltas over a two-week window to catch those outliers.
I'd be curious if your template has a method for evaluating the lock-in factor of the "autonomous" suggestions. Some tools we tested produced recommendations so specific to their own platform that untangling them later became a migration project itself.
throughput first