Guardrails And Safety medium
Your public LLM API limits each key to 60 requests per minute. The inference bill triples anyway, and you trace it to a few users sending 100K-token prompts with the maximum output length, all within their request limit. Which change actually controls cost?
- A Lower the limit to 20 requests per minute per key, scaling request quotas down until monthly spend falls back under budget.
- B Add a global requests-per-second cap at the load balancer so the inference fleet is protected from sudden traffic bursts.
- C Enforce per-user and per-tenant budgets in tokens (input plus output), with caps on context size and max output per call.
- D Cache responses aggressively so repeated large prompts are served from the cache without running inference again.
Answer: C. Enforce per-user and per-tenant budgets in tokens (input plus output), with caps on context size and max output per call.
Cost scales with tokens, not requests, so **token budgets per user and per tenant** are the real control. The price is more complex metering and clients that must handle partial-budget errors. Cutting request counts (A) is tempting but punishes light users while a heavy user still sends huge prompts. Request limits remain useful against connection floods, where every request costs about the same.
One system design question a day, with spaced repetition so the ones you miss come back.
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