Which of the following questions best describes the purpose of rate limiting at AI inference endpoints?

• 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 often a client can send requests within a fixed time window. Its main goal is to prevent abuse, protect backend resources, and maintain predictable latency for all users by enforcing per-client quotas. This helps guard against spikes or misuse that could lead to denial-of-service-like issues or unexpected costs, while keeping access fair. Encryption in transit protects data privacy, but it doesn’t govern how many requests are allowed, so it isn’t describing rate limiting. Some systems may adjust limits based on observed patterns, but the fundamental purpose is to cap request volume per client, not to predict traffic. Rate limiting works alongside authentication rather than replacing it.