Cost optimization controls operational spending while preserving acceptable quality, performance, and scalability in production generative-AI systems. It focuses especially on token usage and infrastructure resources. section 11.5.1 section 11.1
Apply cost optimization through complementary operational controls:
- Trim unnecessary prompt and response text, use efficient encoding, and configure concise outputs without compromising quality. section 11.5.1
- Cache frequently requested, static, or rarely changing information to reduce repeated computation and latency. section 11.5.1
- Monitor token usage and infrastructure spending, then apply quotas or rate limits to prevent unexpected usage spikes. section 11.5.1
- Balance computing, storage, networking, model performance, and scalability rather than optimizing cost in isolation. section 11.1 section 11.7
Production usage controls
A production application can monitor token consumption, configure usage quotas and rate limits, cache repeated information, and adjust resources as demand changes. Teams then evaluate cost against latency, throughput, and required quality. section 11.5.1 section 11.7
