AI economics
Reducing the cost of an AI app.
Find the cost of a useful outcome before deciding which model, prompt or workflow to change.
The challenge
The invoice shows the total. It does not explain the work.
An app can spend money on long context, repeated requests, retries or work that never produces a useful result. Choosing a cheaper model without understanding the workflow can move the cost somewhere else. The question is what the app spends to complete a task well.
The approach
Measure the workflow, then compare changes.
Use representative tasks and a consistent definition of an acceptable answer. Evaluate cost changes alongside the quality and response time the product needs.
Establish a baseline.
Connect requests and retries to a user task. Capture the cost, latency and outcome of that complete path.
Find unnecessary work.
Look for duplicated calls, unused context and repeated processing. Distinguish waste from work that protects answer quality.
Compare under the same conditions.
Test a focused change to the model, context or workflow against the same task set. Include failures and retries in the comparison.
What to verify
A cost decision the product can defend.
A useful result makes the tradeoff visible: what changes in cost, which quality conditions still hold and what happens to latency.
- Cost is tied to completed tasks.
- Quality criteria are explicit.
- The comparison includes retries and failed attempts.