This is a mirror entry of an article I co-authored published in Datadog Engineering Blog.

At Datadog, cost-aware engineering is more than a principle; it’s a performance challenge at scale. We’ve published how we saved $17 million by rethinking our infrastructure, and we’ve built Cloud Cost Management to help customers do the same. But scaling deep, expert-level code optimization across a fast-moving engineering organization presents its own challenge.

Our journey didn’t start with a grand AI design. It began as a mission to trim CPU usage in several critical hot-path functions in our most expensive services. For the hands-on performance engineer, we’ll dig into the gritty work of optimizing Go code: eliminating compiler bounds checks, restructuring loops, and rewriting functions for maximum efficiency. For those building agentic systems with LLMs, we’ll share how those human-driven optimizations seeded the heuristics behind BitsEvolve, our internal agentic system for self-optimizing code.

Whether you’re here for the nanoseconds saved in Go or for a way to scale deep optimization work beyond a handful of experts, we’ll share what worked, what surprised us, and how the art of manual optimization provided the blueprint for an automated system.