Cost engineering keeps LLM spending from becoming an uncontrolled by-product of better prompts, longer context, or unpredictable outputs. It combines token-cost governance with practical testing of models, services, machines, and cloud configurations. section 3.2.10 section 6.3.5
Apply cost engineering by:
- Accounting for input and output tokens, especially when outputs cannot be standardized reliably. section 3.2.10
- Comparing models, services, machine instances, cloud providers, and prompts as a combined system. section 6.3.5
- Testing candidate configurations under load instead of assuming that the fastest or cheapest component produces the lowest total cost. section 6.3.5
A cost-efficiency matrix
Create a matrix of leading configurations, run a service for each combination under load, record throughput such as TPS and operating cost, and translate the results into dollars per token. A newer, more expensive GPU may be cheaper overall when its higher efficiency reduces runtime, particularly for batch inference. section 6.3.5
