A proof tells you a mechanism is correct under stated conditions. It doesn't say what component owns the check, what it costs to run, or what order to build six of them in when a real system needs more than one at once. This post is that build: one engineering answer per problem this series proved, six runbook entries for what on-call actually does when each one fires, and the dependency order that keeps them from being built against each other. Then it turns to the choice every mechanism in this series made without ever arguing for it: decide locally, on a stale view. Checked against seven real points in the centralization design space, not two, from Google's Borg to a production LLM-serving system that arrived at this series' own routing algorithm independently, with a decision tree for finding the right one fast and honest pros and cons for reading past it: real vendor claims kept separate, throughout, from what's actually been verified.
Five posts have priced, over and over, what it costs to guess wrong about a distribution that won't hold still: borrowing five of the Constraint Sequence Framework's six named components along the way, without ever turning the sixth, Meta-Constraint Awareness, on the machinery doing the pricing. This post runs that test on its own series: a real, four-times-computed numerator, a denominator no post has ever measured, and a Return on Investment whose sign comes out genuinely undetermined, not favorably assumed. The formula's own algebra still yields a real bound on how cheap that machinery has to be, and formal metareasoning research, cited directly, explains why the missing stopping criterion isn't an oversight: it's the same unsolvable regress that research already proved exists and resolved by capping it, not computing it. Then it points the same unpriced test at the next post's own proposal (centralizing the machinery itself) before that proposal gets to claim an advantage nobody has costed either.
Every post in this series so far has priced one pool, one resource, one task's decision. Real fleets run hundreds of pools at once, and this post answers whether volume changes what the earlier four prove necessary, not by assumption, but by an exact classical queueing result precise enough to price a real number: how many gigabytes pooling a fleet's own memory margin actually frees, and exactly where that pooling stops working. It also opens a question its own routing mechanism begs and never argues for: why push-based sampling, when a design that removes staleness by construction instead of sampling around it already exists: prices that specific tradeoff, and names the fuller comparison against a fully centralized alternative as the job of the post built for it.