An AI feature that's cheap and fast to prototype can become expensive or slow at real usage scale — and that needs to be part of the design, not a post-launch discovery.
1Cost Scales With Usage in a Way Most Features Don't
A typical feature's marginal cost per use is close to zero. An LLM-powered feature has a real, per-call cost that multiplies directly with usage — a feature that's essentially free to prototype with a handful of test calls can become a meaningful line item at real scale, which needs to shape the design (caching, model tier, scope) from the start.
2Decide the Acceptable Wait, and What Happens Past It
Setting an explicit latency target — and a graceful fallback for the calls that exceed it — turns an unpredictable, occasionally very slow experience into a bounded, designed one. This mirrors the same discipline as designing empty and error states: plan for the case outside the happy path deliberately.
3Step-by-Step Breakdown
Every LLM Call Has a Real Bill Attached. Unlike most feature logic, every single call to an LLM costs real money and takes real time, scaling directly with usage. A feature that's cheap to prototype can become expensive or slow at real scale in ways that need to be designed for upfront, not discovered in a surprise invoice.
Estimate the Real Cost of a Feature at Scale. Practice the habit of translating a feature idea into a rough cost estimate before building it — this is a product decision input, not just a finance afterthought.
Why should a rough cost-at-scale estimate be part of designing an AI-native feature, not just an operations concern after launch?
- →Cost scales directly with usage for AI features in a way it usually doesn't for typical CRUD features, so it can materially affect which design (caching, model choice, scope) is the right one from the start.
- →It shouldn't be a design concern at all — cost is purely a finance team's problem to solve after a feature ships.
Design a Latency Budget. Beyond cost, ask what response time is actually acceptable for this specific feature, and what happens if a call runs long.
Cost and Latency Are Product Requirements. A rough cost-at-scale estimate and a latency budget with a graceful fallback are now part of your feature design, the same as an empty state or an error state. Next: evaluating whether an AI feature is actually producing good results once it's live, not just whether it runs.
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Accessibility (A11y)
1A Slow-Response Fallback Message Needs to Be Announced, Not Just Shown
If a query exceeds the latency budget and the UI shows a 'this is taking longer than usual' message, make sure that message is announced to screen reader users (via aria-live) the same way it's visually shown — otherwise those users are left with silent, unexplained waiting.
<div aria-live="polite">{isSlow ? "This is taking longer than usual..." : null}</div>SEO Implications
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Target 'LLM API cost estimation for product features' and 'AI feature latency budget' as practical planning topics
Readers building real features want a concrete estimation and budgeting method, not just abstract warnings that 'AI can be expensive'.
Best Practices
Estimate Cost at Scale Before Writing the Mini-PRD's Success Metric
A rough cost-at-scale number can change whether a feature is worth building at all, or should be scoped more narrowly — doing this estimate early, alongside the mini-PRD, avoids discovering a cost problem only after significant build investment.
Frequent Bugs
Shipping an AI feature with no caching or rate limiting, where a small number of users making repeated or rapid requests can drive costs far higher than the original estimate assumed.
Add caching for repeated or similar queries and reasonable rate limiting per user as part of the initial build, not as a reactive fix once a cost spike is noticed.
Real-World Examples
The Caching Fix That Cut Costs by Half
A team's FAQ search feature had no caching — identical or near-identical questions from different users triggered a fresh LLM call every time. Adding a simple cache keyed on normalized query text cut LLM costs by roughly half with no perceptible quality loss, since many real questions repeated closely.
const cacheKey = normalize(query);
if (cache.has(cacheKey)) return cache.get(cacheKey);
// else: call LLM, then cache.set(cacheKey, result)