Stork Delivery
Put a spending limit on your AI workflow
Control AI usage at the application level, and measure the cost of a completed task rather than a single model call.
The problem: one AI request can trigger an unpredictable amount of work
An AI feature starts with a simple cost estimate: the price of one model request multiplied by the expected number of users.
Then the application adds retrieval, retries, document processing, tool calls, and human review. A single user request can now trigger a sequence of work whose cost is harder to predict.
Put a budget around the workflow before making it widely available.
A practical approach
Count the complete task
Measure model usage alongside the other work required to produce a useful result. Include processing, storage, external tools, and the effort spent checking or correcting the output where that effort matters to the business case.
Distinguish a completed task from an attempted one. A cheap request that repeatedly fails can be more expensive operationally than a more capable approach that finishes with less review.
Use representative examples, including long documents and ambiguous requests, rather than relying only on the smallest successful demonstration.
Enforce limits where work starts
Set appropriate limits on input size, number of tool calls, retries, run duration, and concurrent work. Apply user or team quotas where necessary to prevent one workload from consuming the shared allocation.
Make the enforcement explicit in the application. A dashboard that reports spending after the fact is useful for visibility but does not by itself stop additional work.
Consider requests already in flight when a limit is reached. The budget mechanism needs a way to reserve or account for work that has started but has not yet reported its final usage.
Decide what the user sees
If a request exceeds a limit, give the user a useful explanation and a next step. They might narrow the input, submit a smaller task, or request an authorized exception.
Avoid silently switching to a weaker process if that could change the reliability of an important result. Any fallback needs its own evaluation and clear boundaries.
A spending cap is an operating control, not proof that the underlying output is correct.
Review usage by outcome
Look for tasks that consume disproportionate effort, repeated failures, and unexpected growth. Decide whether the cause is the model choice, the workflow, the input, or an unclear job definition.
Keep provider pricing assumptions in configuration or operating documentation where they can be updated. The practical aim is an AI application whose cost can be explained and controlled as usage grows, with a sensible response when a task exceeds its intended bounds.
The result: a workflow that respects an enforceable budget
For one task, record the complete usage path and enforce limits before additional work starts. Exercise a long input, repeated failure, and concurrent requests near the quota. Check that in-flight work is accounted for and the user receives a useful next step when a limit is reached. Review cost per completed task alongside correction effort before expanding access.