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LLM Application Cost Optimization Evidence & Implementation Kit

$249.00
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LLM Application Cost Optimization for Production Engineers · inference spend, made adopt-ready · Evidence & Implementation Kit
Cut inference spend without degrading the product, and be able to prove which one you did.
Every control handed to you adopt-ready, from the cost model and per-step token attribution through prompt and semantic caching, model routing, DAG execution and retrieval cost to cost per successful task and enforced budgets, with the evidence an engineering reviewer examines.
Ready in a weekend, not a quarter.

Here is the honest situation. Here is the honest situation. Inference spend is now a line item finance asks about by name, and the answer that satisfies nobody is a single number from a provider dashboard. Most teams optimize the wrong thing. They chase a cheaper model or shave a system prompt while the real spend sits in context that is rebuilt and re-sent on every call, tool-calling loops that re-send the whole conversation, agents that re-plan work they already did, retrieval that returns far more than the task needs, and tasks that fail and get silently retried until one succeeds. Doing this well means instrumenting every call so spend can be attributed per step, per task and per outcome. It means caching with a prefix order and an invalidation policy you can defend, routing by task type, restructuring a serial chain into a bounded graph, and deciding retrieval volume with recall numbers rather than instinct. It means enforcing budgets in code, because a model asked to be brief is expressing a preference. Where teams fall short is predictable: a per-call saving that quietly moved cost onto retries and humans, a cache hit rate that collapsed with no error and no failing test, and a saving reported without the success rate that would have shown the system simply got worse.

This Kit removes the guesswork. It is LLM application cost optimization written as adopt-ready controls you personalize in a weekend, with the evidence an engineering reviewer examines.

What you get, the moment you buy

18
Controls, adopt-ready. Every control, written so you personalize and apply it.
18
Evidence-they-examine checklists. For each control, exactly what a reviewer examines, plus where teams fall short, so you close the gap first.
1
Control Matrix, pre-built. Every control in a working spreadsheet, ready to record status, owner and evidence location.
1
Gap & Readiness Assessment. Score each control and the workbook returns your readiness as a single percentage, and exactly what to fix next.

Grounded in production LLM engineering practice. Editable Word and Excel files.

A cheaper call is not a cheaper system
Cost per call falls the moment you pick a smaller model, even when that model fails twice as often and every retry is billed in full. This Kit builds the attribution, caching, routing and measurement controls that tell you which savings are real, with the evidence a reviewer asks for.

What one control looks like

This is the opening control, where the method begins. All 18 are built to this depth.

LLM-1 Model spend as context times calls times attempts COST MODEL
Put this control in place

Require [your organization name] to express the cost of every production LLM feature as context length multiplied by number of model calls multiplied by number of attempts, summed across every layer that touches a model, and to state which of those three terms dominates that feature before any optimization work is approved.

Control note.

Multiplicative structure beats additive trimming, so halving loop iterations almost always outranks shortening a prompt that was never the problem.

Evidence a reviewer examines
  • A written cost model per LLM feature
  • The dominant cost term named per feature
  • Optimization proposals citing the dominant term
Common finding they raise: Work starts on the most visible lever, usually the model price or the system prompt, before anyone has established which term actually drives the bill.

Why this is not another template pack

  • The evidence is the point. A control you cannot evidence is a gap waiting to be found. This tells you what an engineering reviewer examines and where teams fall short, for every control.
  • The LLM cost specifics built in. Token accounting per layer, per-step and per-outcome attribution, prefix ordering for prompt caching, cache key scope and invalidation, task-type routing and cascade break-even, DAG execution, batching, retrieval recall and reranking arithmetic, and cost per successful task are written into the controls, not left generic.
  • Built on real practice, not one person's opinion, grounded in how production LLM spend actually accumulates and where optimization programmes actually fail.
  • It compounds. This work shares its shape with reliability engineering, capacity planning and AI governance, so it feeds your wider programme.

Who buys this

ML, platform and backend engineers who own a production LLM application and its bill, and the staff engineers, engineering managers and technical leads who have to explain the spend and approve the changes. Whether you are profiling a system for the first time or tightening one already under budget pressure, you save weeks and walk in with your cost model, attribution, caching, routing, retrieval and measurement controls structured.

By the end of the weekend you will have
✓  An adopt-ready control for all 18 areas
✓  A completed control matrix
✓  The evidence a reviewer examines
✓  Token spend attributed per step and per task
✓  A readiness percentage and a fix list
✓  The highest-cost gaps closed

Common questions

Is it really editable? Yes. Word and Excel files you own and adapt. No portal, no subscription.

Does it depend on a particular model provider? No. The controls are written around structure, attribution, caching behaviour, routing and measurement, so they hold whichever provider or model tier you run on.

Does it cover cost per successful task? Yes. The metric, the written definition of success, the independent review of that definition, and reporting savings alongside the success rate are all built as controls.

What if it is not for me? A 30-day money-back guarantee.

Do not answer a spend question with a provider dashboard.
Every control is fast to adopt with the Kit. It is instant, and it is guaranteed.
Add it to your cart and be ready this weekend.

Instant digital download · 30-day money-back guarantee · The Art of Service Pty Ltd, GPO Box 2673, Brisbane QLD 4001 · support@theartofservice.com

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