Guardrails And Safety hard
A public LLM API limits each key to 60 requests per minute. Spend spikes sharply. Investigation shows that a few keys send about one request per second, which is well within the limit, but each request pastes roughly 100K tokens of context and asks for maximum output length. What change actually controls cost?
- A Meter input plus output tokens per key and per tenant, reserving max_tokens at admission and reconciling on completion.
- B Lower the limit to 10 requests per minute per key, which cuts worst-case abusive spend by a factor of six immediately.
- C Require a CAPTCHA at API key creation so automated abusers cannot mint new keys faster than the limits can catch them.
- D Cap the context window at 8K tokens for every key so that no single request can consume a disproportionate share of capacity.
Answer: A. Meter input plus output tokens per key and per tenant, reserving max_tokens at admission and reconciling on completion.
Cost scales with tokens, and token counts per request vary by three orders of magnitude, so request counts cannot bound spend. Reserving max_tokens up front stops concurrent bursts from overshooting the budget. Lowering the request limit punishes small-request users while a single 100K-token call still costs the same. Request limits remain useful against connection floods. A blanket context cap breaks legitimate long-context use without metering cost.
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