Cost-aware AI engineering

AI features have a habit of scaling their cost faster than their value. The good news: most of that spend is avoidable with a few disciplined habits.

Right-size the model

The largest model is rarely the right one. We start from the smallest model that passes our evaluation bar and only move up when quality demands it. Routing simple requests to smaller models can cut cost dramatically with no visible loss in quality.

Cache aggressively

Many requests repeat, exactly or nearly. A layered cache pays for itself fast:

• Exact-match cache for identical prompts
• Semantic cache for near-duplicate questions
• Prompt caching for large, stable system prompts

Make cost observable

If cost is invisible, it will grow. We attribute spend per feature and per user so a regression shows up on a dashboard, not on the monthly invoice.

You cannot optimize a number you never look at.

Stream and truncate

Streaming responses improve perceived performance, and trimming context to what the task actually needs reduces both latency and cost. Small, well-ordered inputs beat large, noisy ones.

Cost is a feature

Treat efficiency as part of the product, not an afterthought. The systems that last are the ones that stay affordable as they scale.