Overview
- Engineers found the main cost driver in live AI systems is pipeline design, not the model, with repeated retrievals, duplicate prompts, oversized context windows and overlapping agent calls multiplying token use.
- A set of production-ready fixes now recommended for teams includes retrieving documents once, extracting only relevant passages before injection, and compressing prompts by moving stable instructions into a cached system prompt.
- Caching delivers big savings when stable prompts or repeated queries recur, using exact-match or semantic response caches and embedding indexes to avoid repeated expensive model calls.
- Model routing and context budgets steer simple tasks to smaller models and reserve large reasoning models for complex work, which cuts per-request cost without notable drops in output quality.
- Teams must add token telemetry, AI FinOps controls, streaming and async pipelines to detect expensive requests and manage tradeoffs such as extraction errors, cache freshness and the extra engineering needed for on-prem or high-volume deployments.