Controlling LLM Costs — Caching, Routing, and Model Tiers
Measure per-feature unit economics
Log tokens per feature per user from day one. Most teams discover one feature (usually free-tier chat) burns 80% of budget. Unit economics per query — not monthly totals — drive every decision below.
The three big levers
Semantic caching answers repeated questions without calling the model (30–60% savings on support bots). Model routing sends easy queries to small models and reserves flagships for hard ones (another 30–50%). Prompt hygiene — shorter contexts, no redundant few-shots — cuts the remainder.
Budgets and circuit breakers
Per-user daily caps, per-feature monthly budgets, and automatic degradation (cached answers, then smaller models, then graceful waitlists) when spend spikes. Alert on cost-per-active-user drift weekly — inference prices fall, but usage grows faster.
Build vs buy check
Managed copilots charge per seat; custom RAG charges per token. Below ~50 heavy users, buy. Above, custom pipelines with caching usually win within two quarters. Model the crossover explicitly.
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Written by AI Engineer
Specialized engineering teams at Omnetra focus on writing high-performance code, ensuring API security, and optimizing layouts for client success.