Token Cost Engineering Introduction 12 of 12 steps written

Which cost reductions cannot cost you an answer, in what order, and when to stop

Twelve steps, four parts, three runnable workloads — seven decisions, four of them free, and a stop between the two groups.

Introduction

You have a model in production and a bill that is larger than you expected. You have heard the standard advice — cache it, trim it, use a cheaper model — and no way to tell which of those applies to your traffic, in what order, or when to stop.

The two ways a cost reduction can fail

Almost everything written about this subject addresses the first and is silent about the second.

#how it failsworst casehow you would find out
1it saves nothing — the move was real and your traffic had nothing for it to act onthe engineering time is gonethe bill does not move
2it costs an answer — the bill fell and something you needed stopped workingyou ship a wrong answeryou might not

The whole of Part 2 is an ordering that keeps those two apart, with a stop between them.

What token cost engineering is

Token cost engineering is the practice of reducing what a workload costs to run without losing the answers you needed from it.

Seven decisions in a fixed order. Four of them cannot change any answer you get; three of them buy money with answers; and decision 4 is the question is that enough? This volume measures all seven on three workloads by holding the model fixed — the tiers are scripted and deterministic — so a change in the outcome can only have come from the decision.

#decisionwhat it askskind
1itemize the billsplit what you are paying for by what was countedrisk-free
2reuse a stable prefixstop paying full price for the same leading tokensrisk-free
3stop paying twiceserve a repeated request from what you already answeredrisk-free
4stop generating earlystop once the answer is completerisk-free
5send only what is neededtrim the context to what the request usesrisk-free
6choose a tiersend what a cheaper tier can answer to the cheaper tiertrades answers
7check, and escalatecheck the answer and re-ask the failures at full pricetrades answers

The three findings, up front

Trimming the context costs money. On two of three workloads send only what the request needs is negative, because trimming breaks the stable prefix that caching was reusing. It does not reduce what caching earns — it takes it to zero. Both moves are recommended everywhere, by everyone, with no mention that they fight.
Two of three workloads are finished before the risky part. They meet a 60% target on the risk-free cuts alone, so no answer is ever traded for money. Almost nothing published on this subject has a stop condition at all.
A safety net can have real recall and no value. On one workload the cheap check catches three of the five requests the cheap tier gets wrong — and they are exactly the three a free rule already saw. Two requests are visible to nothing, and they get a confident, correctly cited, wrong answer.

Is this for you?

if…then
you do not know which half of your bill to attackstep 4, and the answer is probably not the half you think
you are about to cache and trim in the same sprintstep 5. They fight, and nobody says so
you are considering a cheaper modelstep 7 first, then step 6 — you may not need to
you are adding a check to catch a cheap tier's mistakesstep 10. Check which requests it fires on before you build it
somebody asked what a wrong answer costs and you had no numberstep 11. That number decides the whole thing

The three workloads

Twelve requests each, declared by ratios you can measure on your own system. The one that predicts behavior is prefix reuse — which refuted the specification, where the repair workload was expected to be output-heavy because it writes code.

the trafficprefix reusecompetence spreaduntouched bill
A · answertwelve questions over four documents that do not changehighnarrow$3.371
B · fix-a-testtwelve repairs in one module, in a different state each timelownarrow$6.492
C · triagetwelve questions about one failure, across several artifactsmediumwide$4.809

How the measurement works

Scripted tiers, one fixed request order, no clock and no randomness, and every figure reproducible byte for byte from 105 measured configurations. --ablate prints four blocks, and each answers a different question:

blockwhat it doeswhat it hides
the ladderswitch the decisions on one at a time, in orderthat one of them is negative once another is already on
leave-one-outremove each move from the complete procedurenothing — but its savings column recommends deleting your risk-free moves, and step 6 explains why
downshift baselineeverything on the cheap tier, then one router addednothing here — this is where the three routers become comparable
stressvary one premise at a time: the mix, the target, what a rule seesthat the largest single effect on quality is a premise about your requests

Read the savings column and the score column together, always. A configuration that saved more and answered worse did not find a better cut — it took a different exit, and step 6 is where that costs a reader money.

How to read the source labels

labelmeans
conceptargued from first principles, quotes nothing from a lab
measureda lab compares configurations and reports the difference
hands-onyou run it on your own material
Nothing here is a benchmark. Three workloads of twelve requests, scripted tiers whose competence boundary is stated rather than discovered, one token-counting rule that matches no vendor's, and prices that are a plausible shape rather than a quotation. These are comparisons between configurations, not predictions of anybody's invoice. Nothing reads a clock, so no latency claim appears anywhere. Step 12 lists every limit.
Build state: 12 of 12 steps written.