Cost optimization
Systematically reducing inference costs through quantization, caching, routing, and careful hardware choices.
Think of it like
Tightening your budget without cutting corners on quality.
Example
Quantizing models saves 3x on memory, caching saves 50% on redundant queries, using cheaper models saves more.
How it actually works
Cost levers: model size (smaller = cheaper), quantization, caching, batching, regional pricing, and reserved capacity. You optimize by profiling where money goes (what % is model inference vs infrastructure?) and attacking the largest item first.
For product teams
Every 10% reduction in inference cost is margin — even 1% matters at scale.
For engineers
Measure cost per token and per request; profile regularly; quantize early.
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