AI Rate Limiting

Traditional request counting is often insufficient for AI workloads because requests vary greatly in cost. A brief question and a long multimodal task can consume different amounts of compute, while an agent may expand one request into many model calls. Rate limiting therefore combines admission rules with budgets for the work actually performed. Token buckets can permit short bursts while enforcing sustained limits; concurrency caps bound simultaneous activity; task budgets bound an agent's total steps or elapsed time. These controls address resource use, whereas authorization decides whether the operation is permitted at all.

A practitioner identifies scarce resources and selects limits that match them: requests for endpoint protection, tokens for generation, concurrency for worker capacity and spending ceilings for paid services. They assign limits to trusted tenant identities, define retry behavior and instrument rejected or interrupted work. The design should account for queues, streaming cancellation and distributed counters. A useful artifact is a capacity policy that explains which users get priority during saturation and how completed work is charged when a client disconnects.

A rate limit can reject legitimate bursts or encourage retries that worsen overload. Per-IP limits are weak tenant boundaries, and estimated token costs may differ from actual consumption. Limits should be tested with long outputs, recursive agents and abandoned streams, not just small HTTP requests. Rate limiting does not fix inefficient model selection or unbounded internal loops unless those operations participate in the same accounting system.