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Your health check says everything's fine while the model behind it hasn't even finished loading yet.
What you'll have at the end
A /health and /ready endpoint pair, proven by killing one dependency and watching traffic get pulled from rotation
You need
A model-serving app already deployed behind something that can pull an instance out of traffic on its own signal: a container orchestrator, a load balancer, or an auto-scaling group, plus a way to redeploy it with new code.
Not covered
Building the dashboards or alerts these checks would feed into, and probing every downstream provider's own uptime beyond what this one instance needs to serve a request.
Load test your app before real traffic finds the bottleneck
The natural next check once this pair exists: watch it behave under sustained concurrent load, a harder test than one kill-and-restore drill.
Put every model call behind one gateway that routes, caches, and falls back
A gateway sitting in front of several instances can read this same readiness signal to decide which instance gets the next request.
See your LLM's cost, latency, and errors on one dashboard
Where a pattern of repeated readiness failures belongs once the check itself is proven: charted over time on a dashboard, rather than read one pass-or-fail probe at a time.
Checked 18 Aug 2026
Part of the AI Engineering cookbook