Apply Backpressure to AI API Workloads

This article provides a production guide for implementing backpressure controls on AI API workloads. The key points are:

- Apply backpressure before the AI dependency is saturated to avoid performance degradation

- Define and enforce three control points: max_inflight (max concurrent in-flight requests), bounded_queue (limit the size of the request queue), caller_deadline (respect the caller's timeout)

- Reject overload conditions early without waiting for upstream attempts. Bound queue size and monitor queue depth

- Reject work that cannot complete within the caller's remaining deadline. Distinguish overload from permanent errors

- Make rejection, cancellation, retry, fallback, and settlement observable. Record important metrics like admission_key, inflight_limit, queue_limit, deadline handling and shedding strategy

- Use deterministic test cases with reviewed values rather than inferred defaults. Freeze configuration and observable baseline

- Roll out to a small cohort with an explicit owner and rollback plan. Monitor overload signals and adjust thresholds

- Failure amplification is a release blocker - unbounded queues, ignoring deadlines, global pools, and retry storms can cause further issues. Monitor success & harm together

The guide emphasizes defining explicit controls and failure tests rather than relying on vague performance metrics. It provides a reusable artifact for evaluating and maintaining backpressure controls on AI APIs.