Operational Efficiency and Optimization for GenAI Applications
Token, Model, Capacity, and Cache Optimization
Optimize cost without sacrificing required quality by controlling context and output tokens, routing by complexity, caching safely, batching suitable work, and matching capacity to traffic.
Concepts
- Track input, output, cached, and retrieved tokens by feature, tenant, and model to identify cost drivers.
- Route simple work to smaller models and escalate only requests that require greater capability.
- Semantic caches need tenant, authorization, model, prompt, and freshness boundaries to avoid leakage or stale answers.
- Provisioned capacity is economical only when sustained utilization and service-level objectives justify commitment.
- Prompt caching reuses stable prefixes on supported on-demand models; track cache writes, reads, misses, latency, and model-specific limits before claiming savings.
Exam tips
- Reducing irrelevant retrieved context can improve both cost and answer quality.
- Cache keys must include every factor that can change an authorized answer.
- Compare price-to-performance on representative work, not price per token alone.