LLM Cost Control: Frequently Asked Questions
llmops
Straight answers on token spend, caching, model routing, and how to stop an LLM feature from quietly burning your budget.
Token costs sneak up on every team running LLMs in production. These are the questions we hear most from people trying to keep spend predictable without nuking quality.
Where does most LLM spend actually go?
Q: Is it the model price, or something else?
The sticker price per million tokens is rarely the problem. The real cost drivers are: long system prompts repeated on every call, retrieved context that's too large, agent loops that retry on failure, and chatty users on the free tier. Audit those four before you negotiate model pricing.
Q: How do I attribute cost to a feature or customer?
Log tokens in, tokens out, model name, and a tenant ID on every call. Roll those up nightly. Without per-tenant attribution you can't tell whether one power user is eating your margin.
Caching
Q: Prompt cache or semantic cache — which should I use?
Prompt caching reuses tensors for an exact token-level prefix. It's safe, deterministic, and cheap to add. Start there. Semantic caching reuses outputs for similar queries — bigger savings, but you need a freshness check and a way to invalidate when sources change.
Q: When does caching backfire?
When the cache key forgets the tenant ID. One user sees another user's answer, and you have a security incident, not a savings story.
Model routing and quantization
Q: Should I route easy queries to a smaller model?
Yes, if you can classify cheaply. A small router model or even a keyword check can send simple queries to a smaller, cheaper model and reserve the flagship for hard ones. Watch for quality regressions on the boundary cases.
Q: Is quantization worth it for cost?
For self-hosted models, yes. FP8 is near-lossless. INT8 is usually safe. INT4 (AWQ, GPTQ) cuts memory hard but can collapse on reasoning tasks — test on your evals before committing.
Budgets and alerts
Q: What's the minimum viable budget setup?
Three ceilings: cost per request, iterations per agent loop, calls per tool. Terminate on any cap hit. Add a daily spend alert at 2× expected and a hard monthly cap that pages someone.
If you'd rather skip the cost-engineering work, SNAP AI — no-code Agent Maker runs the agent on your sources with the budgets already wired in.