EP177: AI API Streaming Backpressure — Keep Tokens Flowing Without Melting Clients

Build deadline-aware admission and bounded streaming buffers with resource-specific permits, tenant fairness, honest overload responses, and realistic slow-consumer tests.

A practical guide to admission control for AI APIs: classify work, reserve capacity, shed safely, protect interactive traffic, and make overload decisions visible before queues become outages.

A practical guide to graceful degradation for AI APIs: define essential outcomes, reduce optional work, preserve contracts, and keep user journeys useful when models or providers are slow, expensive, or unavailable.

A practical guide to recovery objectives for AI APIs: define acceptable data loss and recovery time, prioritize workloads, preserve routing policy, reconcile state, and test restoration before an incident.

A practical guide to verifying AI API recovery: test real user journeys, validate routing and billing state, drain queues safely, compare quality and latency, and avoid declaring an incident over before the system is truly healthy.