Which statement about rate limiting at AI inference endpoints is correct?

• A. Rate limiting is used to ensure data privacy by encrypting in transit.
• B. Rate limiting uses machine learning to predict traffic.
• C. Rate limiting helps prevent abuse by limiting request rate per client.
• D. Rate limiting replaces authentication.

Answer: (C)

Rate limiting at AI inference endpoints is about controlling how many requests a single client can send in a given time frame. This constraint helps prevent abuse by stopping any one client from flooding the system, which could degrade performance for everyone, drive up costs, or cause outages. By associating limits with a client identity (like an API key or IP), the service can throttle or block excess requests after a threshold is reached, preserving availability and reliability for all users. It’s not primarily about encrypting data in transit or about predicting traffic with machine learning, though some systems may adapt limits over time; the core purpose remains to cap the rate to stop abuse and maintain fair access. It doesn’t replace authentication; you still identify clients, and rate limiting works alongside authentication as a usage control measure.