Cost per model request is only one part of AI operating cost. Retrieval, storage, retries, tool calls, monitoring, and staff review also matter. Optimize cost per successful task rather than choosing the cheapest token price in isolation.
A practical scenario
A document assistant may repeatedly send an entire contract when only a few clauses are relevant. Better retrieval can reduce context size, but shortening context too aggressively can remove the exception that makes the answer correct. Compare cost and evaluated answer support together.
Design the first version
Set a maximum number of steps, a token budget, and a timeout for each task type. Route simple extraction to a suitable smaller model only after testing it on representative inputs. Cache stable, permission-compatible results using content versions, and avoid sharing personalized answers across users.
What to test and measure
Measure costs by intent and outcome. A cheap workflow with frequent human correction may be more expensive overall. Include an unavailable-provider scenario and a budget-exhausted fallback. Revisit budgets as usage changes, and keep the original quality test set so cost changes do not silently degrade the product.
Questions to resolve before commissioning
- Which source system owns the facts used in this workflow?
- Who reviews exceptions and corrects inaccurate output?
- What baseline and acceptance criteria will determine whether the pilot is useful?
- What should the user do when a source, tool, or device is unavailable?
Explore the implementation
This is a planning guide, not a report of measured client results. Examples are illustrative. Explore the related SyntaxLab demo to discuss the interaction, then use your own records and acceptance criteria for a production pilot. Discuss a scoped project or review our AI automation services.
Further reading
Read Microsoft guidance on AI evaluation for technical background. Continue with AI Invoice Processing: Extraction, Validation, and Approval Controls.