Chapter 4 · Operational Efficiency and Optimization for GenAI Applications·v1.0.0·Updated 6/22/2026·~11 min
What's changed: Initial: 3 sections for Domain 4 (operational efficiency and optimization)
4.1Cost Optimization and Resource Efficiency
Key points
Learn strategies to reduce FM cost: token efficiency (context optimization, compression, pruning), model tiering, prompt caching/semantic caching, batch inference, and provisioned throughput optimization.
FM cost is driven mainly by token volume. Cut input/output tokens, avoid unnecessary calls, and tier models by difficulty to lower cost while preserving quality.
4.1.1Levers to reduce cost
- Token efficiency: optimize the context window, compress prompts, prune context, and limit response size to cut tokens.
- Model tiering: route by query complexity between small/cheap and large models (choose by price-performance).
- Caching: avoid unnecessary calls with prompt caching (reuse common prefixes) and semantic caching (reuse results for semantically identical queries).
- Batch/reserved: process bulk cheaply with batch inference, optimize provisioned throughput for steady load, and monitor utilization with Auto Scaling.
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