One thing that's easy to overlook is that rate limiting alone doesn't solve this problem. Even if requests stay within provider quotas, a few long-context or multi-agent workflows can still monopolize compute and hurt interactive traffic.

We've had better results by treating AI workloads like different service classes: user-facing requests get reserved capacity and tighter latency budgets, while batch jobs are throttled, paused, or resumed based on system load. It also helps to track token usage and queue depth per workflow instead of only requests per minute.

As AI products grow, scheduling and prioritization become just as important as prompt quality.