Cost Optimization for LLM APIs at Scale (EXPLAINED)
Hard scenario on LLM cost control — token budgeting, routing, caching, batch APIs, and unit economics at millions of queries.
TL;DR — Quick Answer
LLM cost ≈ (input tokens + output tokens) × price per token × query volume, plus retrieval/embeddings. Optimize by: routing simple queries to cheaper models (cascade), aggressive prompt compression, limiting output length, semantic/prompt caching, batch offline jobs, fine-tuning smaller models for high-volume tasks, caching embeddings, and tracking cost per successful task (not per call). Set budgets per tenant, autoscale down on duplicate queries, and measure quality regression when cutting cost.
The Interview Question
How do you optimize LLM API costs at scale without destroying quality? Walk through model selection, caching, batching, and architecture choices.
Deep Explanation
Cost drivers
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