Technical topic
AI cost engineering
Outcome units, request ledgers, model routing, caching, capacity, and the cost of failed attempts.
Direct answer
AI cost engineering measures the cost of accepted outcomes rather than optimizing token price in isolation. It attributes model calls, tools, retries, latency, review, failed work, and reversals to a defined result, then uses that evidence to make routing, caching, context, and capacity decisions without hiding quality regressions.
What this topic helps you decide
AI cost per outcome
Choose a business or engineering result as the denominator and retain failed and repaired attempts.
Model routing and caching
Route or cache only where task-family evidence shows that quality and serious-failure risk remain acceptable.
AI cost release gates
Put unit economics, reliability, review effort, and rollback criteria into production decisions.
Practical questions answered
- Why did token cost fall while cost per resolved task rose?
A unit-economics diagnosis for AI products where cheaper inference is offset by retries, failures, latency, escalation, and manual repair.
- How do I calculate cost per AI outcome?
A fully loaded outcome-ledger method that includes accepted, rejected, reversed, and reviewed AI work instead of dividing one provider invoice by request count.
- How can I shorten context without damaging task quality?
An evidence-preserving method for reducing LLM context through necessity tests, structured compression, retrieval, and paired quality evaluation.
- When should I route to a smaller or larger model?
A task-family routing experiment that compares model policies at equal quality, escalation, refusal, latency, cost, and serious-failure conditions.
- What should an LLM application cache?
A decision framework for prompt prefixes, retrieval results, tool reads, embeddings, responses, and derived artifacts under staleness and privacy constraints.
- How should I choose between on-demand and provisioned AI capacity?
A capacity decision method that uses arrival patterns, service levels, utilization ranges, commitment risk, and failure cost instead of average tokens.
- What belongs in an AI cost release gate?
A release record that ties AI unit cost to quality, reliability, sensitive-data handling, capacity, fallbacks, owners, and rollback thresholds.
Go deeper with a field guide
AI Cost Engineering
Token Economics, Model Routing, Caching, Capacity, and Cost per Outcome
Explore AI Cost EngineeringRelated books
Reusable resources
- Cost-per-outcome ledger (CSV)
- Agent evaluation task card (YAML)