AI feature costs blow up when scope is vague. Control them by defining the exact job the AI does, estimating per-request token usage before you build, and adding guardrails, caching, limits, and fallbacks, so a single feature cannot run away with your API bill. Scope the cost as deliberately as the functionality.
Start with the job, not the model
Define the narrow task the AI performs and what "good" output looks like. A tightly scoped job (summarize this, classify that) is cheaper, faster, and easier to evaluate than an open-ended assistant. Most features need far less model than teams assume.
Estimate token costs upfront
Before building, estimate input and output tokens per request and multiply by realistic volume. This one calculation tells you whether the feature is viable at scale, and often reveals that a smaller model or a tighter prompt is the difference between profitable and ruinous.
Add guardrails
- Caching: reuse results for repeated or similar requests.
- Limits: cap tokens, rate-limit per user, and truncate inputs.
- Fallbacks: degrade gracefully when the model is slow, down, or unnecessary.
Prototype cheap before committing
Test the prompt and model on real examples before wiring it into production. A short evaluation tells you whether a cheaper model is good enough, which is usually the biggest lever on cost.
Choose the right model tier
Match the model to the job: use a smaller, cheaper model for routine tasks and reserve the largest models for genuinely hard ones. Mixing tiers by task keeps quality high where it matters and cost low everywhere else. See how we build AI-assisted product features.