Token costs add up quickly at scale. The good news: most spend comes from a handful of patterns you can engineer away—without hurting quality.
Route work to the right model
Use small, fast models for simple tasks and reserve premium models for hard queries. Add confidence thresholds and fallbacks. Measure quality and cost per use case, not just overall.
Cache what repeats
Introduce both embedding caches and response caches with sensible TTLs and cache keys. Precompute frequent prompts offline. Log cache hit rates and investigate misses.
Control context
Summarize or prune long contexts; cap input and output tokens; and prefer structured outputs (JSON schemas) to reduce retries and parsing overhead. Compress prompts by removing redundant instructions.
Batch and stream
Batch batchable tasks on the server to minimize setup costs, and stream tokens to the UI so users feel latency drop even when total time is unchanged.
Do these four things well, and you’ll typically cut costs 30–60% while keeping quality steady—or even improving it.