AI Dictionary › AI Fundamentals
Rate limiting is the restriction of how many requests a client can make within a time window: a hundred per minute, ten thousand per day. It protects services from overload, abuse and runaway costs, ensuring shared resources remain fairly available to everyone.
The most common algorithm is the token bucket: a bucket fills with tokens at a constant rate, each request consumes one, and when the bucket is empty requests are rejected with HTTP error 429 (Too Many Requests). Variants like sliding window and leaky bucket trade precision against simplicity differently. Responses often include headers showing remaining quota and reset time.
In AI, rate limiting is a daily experience: model APIs enforce limits on requests per minute and tokens per minute, tiered by subscription level, and application builders must handle them with queues, backoff and load spreading across multiple keys or providers. Beyond AI it governs public APIs, logins (against brute force attacks) and network traffic.
The concept descends from traffic shaping in telecommunication networks: the token bucket and leaky bucket algorithms were developed and analyzed in the 1980s, notably in the community designing ATM networks, to regulate packet flows. With the API economy of the 2000s, rate limiting moved from the network layer to the application layer, becoming a commercial instrument too: paid plans differ precisely in the limits they grant.
Grace's professional scenarios on AI applications in production include managing API limits: being able to name token buckets and quota windows makes your prompts read like an architect's. The daily gym gets you used to working well within defined constraints, the essence of rate limiting.
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