Brian Mullins is the Head of Engineering at Diaceutics, a platform company improving patient identification in precision medicine. He brings a technically grounded, detective-minded approach to FinOps — one built on granular telemetry, unit cost modeling, and the discipline of not optimizing prematurely.
This episode covers how Brien's team doubled platform query volume while holding costs flat by applying the same cost visibility and attribution practices to AI workloads that they already had in place for traditional cloud infrastructure. He walks through real examples: eliminating $6,000/month in redundant processing, catching a warehouse left on the wrong tier, and using unit cost experiments at 10, 100, and 1,000 records to project AI spend before it scales. He also makes the case that cost anomalies are not just a FinOps signal — they are a security signal too.