What Auditable AI Actually Costs: The Engineering Economics of Reproducibility Infrastructure
Enterprise AI teams keep deferring reproducibility infrastructure because they believe it's expensive. The numbers don't support that belief. Here's a defensible cost model for the runtime audit-trace persistence, environment fingerprinting, and attribution-stability measurement that regulated AI deployments will need over the next three years.
A common pattern in conversations with enterprise AI engineering teams: someone — usually a senior engineer or an MLOps lead — agrees that reproducibility infrastructure for production LLM deployments is becoming necessary, then immediately follows with a deferral. "We'll build it when the regulator forces us. Right now we can't justify the cost."
That deferral rests on an assumption nobody validates. The assumption is that reproducibility infrastructure is expensive. In conversations where I've pressed on actual numbers, the responses range from "I'd guess maybe six figures a year" to "honestly I have no idea, but it sounds like a lot of engineering time." Neither answer is...
Copyright of this story solely belongs to hackernoon.com. To see the full text click HERE
