A checklist is only useful if you actually run it, so this one is built to be short enough to use and specific enough to matter. Every item includes a one-line justification — the reason it's on the list — because a checklist whose items you don't understand is one you'll skip under pressure.
Run the pre-launch section before you ship any AI workload. Run the production section once it's live and then quarterly, because prices, models, and your own usage all drift. None of these items requires specialized tooling; they require attention and a willingness to do the arithmetic.
Print it, copy it into your project tracker, or keep it open in a tab. The format is deliberately scannable so it survives contact with a busy team.
The checklist is organized into four sections that map to the lifecycle of an AI workload: pre-launch, model selection, production monitoring, and optimization review. Each section is a gate at a different moment — before you ship, when you choose a model, while it runs, and on a recurring schedule. Treating them as distinct gates rather than one undifferentiated list is what keeps the right checks happening at the right time.
Pre-Launch Checklist
Work through these before any AI feature reaches production. They catch the design-level mistakes that are cheap to fix now and expensive to fix later.
- [ ] You've estimated per-request cost from a representative request. Without a baseline number, you have no idea whether the design is affordable. Our Step-by-Step Approach walks through this.
- [ ] You've scaled the estimate to expected volume and stress-tested at 3x. Usage grows faster than teams expect; a budget that only survives at launch is no budget.
- [ ] You've chosen the smallest model that passes quality testing. The price spread within a family is 10x to 30x — defaulting to the flagship is the costliest common mistake.
- [ ] You've identified the stable prefix of your prompt for caching. Repeated content billed at full price thousands of times is the largest avoidable waste.
- [ ] You've set an explicit maximum output length. Output costs three to five times input; unbounded responses are expensive verbosity.
- [ ] You've trimmed context to the minimum that holds quality. Every context token is billed on every request; extra context is compounding waste.
- [ ] You've flagged any latency-tolerant work for batch processing. Batch APIs run at roughly half price for identical output.
Model Selection Checklist
Revisit these whenever you pick or change a model. Model choice is the highest-leverage cost decision you make.
- [ ] You've matched model tier to task difficulty, not picked one model for everything. Most workloads mix hard and easy calls; routing by difficulty captures the biggest savings.
- [ ] You've tested the cheaper model before assuming you need the expensive one. Quality assumptions are often wrong; measure before you pay the premium.
- [ ] You've compared input and output rates across candidate models. The cheapest input price isn't always the cheapest total if output rates differ. See our Complete Guide for how families differ.
Production Monitoring Checklist
Once live, these keep cost observable so a spike is a signal you catch, not a surprise you absorb.
- [ ] Every request logs token counts and the model used. You cannot optimize what you cannot measure.
- [ ] Spend is tagged by feature. A single total bill can't tell you which feature to fix when costs move.
- [ ] You have a budget alert set at around 70 percent. Alerting before you hit the cap leaves time to react instead of explaining an overage.
- [ ] Prompt caching is confirmed active, not just configured. Caching that silently fails to apply gives you the cost without the saving — verify hit rates.
- [ ] You review the top three cost-driving features monthly. The bulk of spend usually concentrates in a few features; that's where optimization pays.
Optimization Review Checklist
Run this quarterly. The price-performance frontier shifts, and yesterday's optimal choice drifts out of date.
- [ ] You've re-tested current tasks against newer, cheaper models. Capabilities migrate down to cheaper tiers over time; you may be over-provisioned.
- [ ] You've re-run cost estimates with current prices and volumes. Both your usage and provider pricing change; stale estimates mislead.
- [ ] You've checked context size hasn't crept up since launch. Context tends to accumulate quietly as features are added — re-trim it.
- [ ] You've audited new workloads added this quarter for batchability and caching. Optimizations applied at launch don't automatically extend to features added later. Our Best Practices article details these in depth.
How to Use This Checklist
Treat the four sections as gates at different points in a workload's life. The pre-launch and model-selection sections are gates before shipping — don't deploy with unchecked boxes. The production section is a continuous gate, wired into your monitoring. The optimization section is a recurring gate, ideally on a calendar reminder.
The common failure is running the checklist once, at launch, and never again. Cost discipline isn't a launch task; it's a maintenance habit. The teams that stay efficient are the ones that rerun the production and optimization sections on a schedule, catching creep before it becomes a crisis like the one in our Case Study.
A practical way to keep this alive is to assign ownership. Cost that belongs to everyone belongs to no one, and the checklist quietly stops being run. Name a single person responsible for the quarterly optimization review and another for watching the production alerts. Put the quarterly review on a recurring calendar invite so it survives busy weeks. When a new AI feature is proposed, make the pre-launch section a required item in your design review, the same way a security or accessibility check might be — that's how you stop unestimated workloads from slipping into production and reintroducing the very creep this checklist exists to prevent.
Frequently Asked Questions
How often should I run the full checklist?
Run the pre-launch and model-selection sections before every new workload ships. Wire the production section into continuous monitoring. Run the optimization section quarterly. The mistake is treating the whole thing as a one-time launch exercise rather than an ongoing maintenance habit.
Which checklist item has the biggest impact?
Matching model tier to task difficulty and confirming caching is active. Those two together typically account for the majority of available savings, because flagship overuse and uncached repeated prompts are the two largest sources of waste in most workloads.
What does "verify caching is active" mean in practice?
Configuring caching and actually getting cache hits are different things. Check your provider's reported cache hit rate or the cached-token counts in API responses. If your prompt structure changes per request, or the stable prefix isn't first, caching can silently fail to apply, leaving you paying full price.
Do I need special software to run this checklist?
No. The estimates need only a calculator and a pricing page. The production items need token logging, which most provider APIs expose directly in their responses, so capturing them is a matter of recording fields you already receive. No dedicated cost-management platform is required to start.
Why stress-test at 3x volume specifically?
Because AI usage reliably outpaces launch projections once a feature proves valuable, and engagement-linked drivers compound. The 3x figure is a pragmatic buffer that catches budgets which only survive at initial volume. If your design is still affordable at 3x, you have real headroom.
Key Takeaways
- Run pre-launch and model-selection sections before shipping; don't deploy with unchecked boxes.
- Estimate per-request cost, scale it, and stress-test at 3x volume.
- Default to the smallest model that passes quality testing and confirm caching actually applies.
- Cap output, trim context, and flag latency-tolerant work for batch.
- Tag spend by feature, log tokens, and alert at 70 percent of budget.
- Rerun the production and optimization sections quarterly to catch creep early.