A field-tested, 47-point checklist used by engineering leaders to audit production AI workloads and reduce inference spend by an order of magnitude — without sacrificing quality. Built from 20+ enterprise engagements across logistics, fintech, and SaaS.
Most production AI workloads can be cut to one-tenth or less of their current inference cost — without sacrificing quality. The savings rarely come from a single trick; they come from disciplined work across seven specific layers, in a specific order. This 47-point checklist is the operational sequence we use on enterprise audits.
Across the audits we run, the typical headline number is an 8×–20× reduction in inference cost, depending on workload type and starting baseline. The variance comes from what is leaking — orchestration cleanup tends to deliver the largest single improvement, prompt and context discipline the second, and model routing the third. Caching, infrastructure, and UX changes round out the rest.
In our experience, yes. Prompt caching and model routing typically capture roughly a third of the available savings. The other two-thirds live in orchestration, context discipline, UX surface decisions, and governance — none of which are addressed by either technique. The audit usually starts with whichever layer is leaking most, not whichever layer is most fashionable.
The break-even point sits roughly where ongoing inference spend has crossed a five-figure monthly threshold and is still growing. Below that, internal optimisation by the engineering team usually pays back faster than an external engagement. Above it, the variance between an audited and an unaudited workload tends to be large enough that the audit pays for itself inside the first quarter.