All 7 articles, sorted alphabetically
Per-Feature Degradation Ladders: Planning Every Step Before an Outage
Write an ordered fallback ladder for each AI feature before the provider fails: secondary model, small model, cache, rules and an honest message. Decide which features must never use a weaker model, how modes switch without flapping, and how to drill it.
Read article →Timeouts for Streaming LLM Calls: First-Token, Idle and Total Budgets
One total timeout kills long streamed answers and still waits too long on stalled ones. Split it into a first-token budget, an idle budget between content events and an overall deadline, and see what SDK defaults, pings and thinking phases do.
Read article →Designing an AI Incident Response Runbook
A runbook for incidents that return HTTP 200: an AI incident taxonomy, severity rules that escalate safety and privacy, containment levers built in ad…
Read article →Circuit Breakers and Backpressure for Model APIs
How to protect a service that calls a model API from overload and slow failure: Little's law with token-variable requests, token-weighted admissi…
Read article →Multi-Provider Model Routing and Graceful Degradation
What it takes to make a second model provider a real fallback: eligibility filters, request and response adapters, error classification and circuit br…
Read article →Multi-Region Inference and Provider Outage Recovery
The recovery lifecycle for AI features across regions: mapping every regional dependency beyond the model, RTO and RPO per AI asset, static stability,…
Read article →SLOs for AI Applications: Quality, Latency, Availability, and Cost
How to define service level objectives for an AI feature: usable-answer availability, streaming latency, sampled judged quality with confidence interv…
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