TRAIL
Running AI in production
Cost, latency, fallbacks, monitoring, and the first hour of an incident — what keeps a shipped system reliable.
Shipping is the easy part; staying reliable under real, messy, high-volume usage is where most of the engineering discipline actually lives. This trail covers what a production AI system needs that a prototype doesn't.
By the end you'll be able to name your system's cost and latency levers, design a fallback for when a model provider goes down, catch quality drift before users report it, and know the first move in an incident before you're in one.
Begin trail →- 1BuildLogging and traces
The prerequisite for everything else here: if a bad output isn't traceable back to its cause, nothing downstream in this trail can be diagnosed.
- 2BuildCost controls
Inference cost scales with usage in a way most software cost doesn't. The levers — caching, tiering, trimming — are design decisions, not afterthoughts.
- 3BuildLatency budgets
Set the budget from where the feature sits in the product, not from what's technically achievable, then choose an architecture that can meet it.
- 4BuildFallbacks and graceful degradation
Model APIs go down. A system with no fallback path fails as hard as its least reliable dependency.
- 5BuildMonitoring in production
Uptime monitoring misses the failure mode specific to AI: the service is up and the answers have quietly gotten worse.
- 6BuildIncident response for AI systems
When something does go wrong: contain first, diagnose second, and feed the incident back into your evaluation set so it can't happen the same way twice.
- 7BuildSafe deployment
Ship to a slice of traffic first. The staged rollout is what catches the failure your evaluation set didn't anticipate, before it reaches everyone.