Price the workflow, not the token
A low token price can still produce an expensive system if prompts carry unnecessary context, users retry weak outputs or every request calls the largest model. Begin with the volume, latency and quality requirements of the complete workflow.
Separate predictable processing from tasks that genuinely benefit from generation. Extraction, routing, validation and caching can often be handled deterministically.
Use a model portfolio
Different steps need different levels of reasoning, context and reliability. Route simple tasks to smaller models and reserve stronger models for the moments where quality changes the business outcome.
- Measure input and output volume by step
- Cache stable context and reusable results
- Set structured output contracts
- Evaluate before changing models or prompts
Optimise the cost of failure
The cheapest answer is not economical if it creates manual correction, customer risk or a second workflow. Track accepted outputs, review time, retries and downstream errors alongside infrastructure cost.
Cost control becomes durable when it is part of product telemetry and evaluation, not a one-off procurement exercise.
Technology capabilities and commercial terms change over time. Validate current provider documentation and test assumptions against your own workload before making an investment decision.
