Define Cost Boundaries Before Launch
Before launching an AI feature, teams often focus on quality, latency, and reliability. Cost boundaries are easier to overlook until real users reveal long prompts, long outputs, retries, expensive fallback models, or missing logs.
Use this checklist before sending production traffic to an AI API.
1. Is Model Choice Tiered?
Do not send every request to the strongest model by default. Confirm:
- default low-cost model
- stronger model for complex tasks
- fallback behavior
- whether fallback routes to a more expensive model
- background batch jobs separated from user-facing requests
Check the model pricing table first, then read Reasoning Models vs Text Models and how to choose a low-cost AI model before deciding which tasks need a stronger model.
2. Is Token Budget Based on Real Samples?
A budget should not rely only on theoretical prompt length. Sample real requests and record:
- average input tokens
- P90 input tokens
- average output tokens
- P90 output tokens
- accumulated length in multi-turn conversations
If you do not have samples yet, use conservative assumptions and calculate multiple scenarios in the text model calculator. For a spreadsheet-like record of assumptions, use the AI app token budget template.
3. Has Cache Hit Rate Been Verified?
If your budget depends on prompt caching, confirm that the cache structure is stable. Check:
- fixed prompts are truly fixed
- dynamic variables do not break the cache prefix
- tool descriptions are suitable for caching
- real hit rate is logged
Do not place an unverified 80% hit rate into the official budget.
4. Are Retries Capped?
Retries improve reliability, but they also multiply cost. Confirm:
- SDK automatic retry count
- queue retry count
- whether timeouts rerun the full request
- whether page refreshes submit duplicate requests
- idempotency or deduplication strategy
For long-output tasks, one failed retry may mean paying for the whole output again.
5. Are Logs Good Enough for Billing Review?
At minimum, log these fields:
| Field | Why It Matters |
|---|---|
| model | confirms the expected model was used |
| input tokens | explains input cost |
| output tokens | explains output cost |
| cache hit tokens | measures cache savings |
| request id | detects duplicate calls |
| feature name | separates cost by product area |
Without these fields, billing surprises are hard to explain.
6. Are Billing Alerts Set?
Set daily, monthly, or growth-rate alerts before launch. Use baseline, growth, and stress budgets instead of one end-of-month threshold.
7. Is There a Fallback Plan?
If cost exceeds expectations, know what to do:
- temporarily switch to a lower-cost model
- limit maximum output length
- reduce retry count
- pause non-core batch jobs
- add quota limits to high-cost features
Prepare the fallback before the bill spikes, not after.
Summary
A pre-launch AI cost checklist should cover model tiers, token budget, cache hit rate, retries, logs, alerts, and fallback plans. When these boundaries are clear, a team can launch with more confidence and diagnose cost changes quickly after real traffic arrives.