Do these 3 things before closing this tab:
1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteA health check can take healthy AI servers out of service in two different ways: a liveness failure can restart a container, while a readiness failure can leave it running but remove it from Service traffic. The title does not identify the specific implementation or incident, so its cause cannot be established. The documented failure pattern is clear, though: a check that mistakes temporary unavailability for a need to restart—or makes many instances fail together—can turn a recoverable problem into an outage.
How a health check can make a healthy server unavailable
In Kubernetes, probes are not interchangeable status lights. Each one has a different job and a different consequence when it fails. The critical question is whether the check is deciding that a process must be restarted, that a Pod should stop receiving traffic for now, or that startup is still in progress.
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| Probe | What failure means | What Kubernetes does |
|---|---|---|
| Startup | The application has not yet completed initialization. | Until the startup probe succeeds, Kubernetes holds off on the configured liveness and readiness probes. |
| Liveness | The application is in a state that requires a restart. | After the configured failure threshold is reached, Kubernetes restarts the container. |
| Readiness | The application cannot serve traffic right now. | Kubernetes keeps the container running but does not send it traffic through Services. |
Kubernetes warns that “Incorrect implementation of liveness probes can lead to cascading failures.” The distinction and probe behavior are documented in the Kubernetes probe configuration guide.
Why AI inference changes the recovery cost
An inference server may need to download model files, load weights, and initialize accelerator resources before it can serve requests. A restart during this work can repeat that initialization and keep the instance unavailable longer. That makes a liveness check especially consequential: it should indicate a state that genuinely calls for restarting the process, not simply that the server is temporarily busy or not ready.
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Should readiness and liveness probes be the same?
Usually, they should answer different questions. Readiness is the traffic decision: can this instance accept work now? Liveness is the restart decision: is the process stuck or otherwise unable to recover without restarting? If a slow model load or momentary overload makes one shared endpoint fail, using that failure for liveness can restart a process that only needed more time; using it for readiness can correctly stop new traffic while the process recovers.
Also consider what the endpoint checks. A liveness probe that depends on a database or other remote service can restart otherwise functioning application processes because of a problem outside the Pod. AWS advises keeping liveness independent of such external dependencies. Readiness needs care too: AWS warns that an externally dependent readiness probe can make all Pods fail readiness, causing an outage and potentially cascading failures in services that rely on them. Its guidance says, “a poorly configured readiness probe can cause an outage instead of preventing it.” See AWS probe and load balancer guidance.
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How to keep Kubernetes from restarting a model server while it loads
Use a startup probe when initialization can take longer than the ordinary liveness or readiness checks allow. Kubernetes does not run the other probes until startup succeeds, giving a slow-starting application a defined window to become ready without being treated as dead. Set that window based on the model-serving workload, including model download and accelerator initialization time, rather than assuming a generic application startup time.
Google’s GKE Inference Gateway tutorial shows one vLLM configuration: a startup probe permits up to 600 failures at one-second intervals (a ten-minute window); liveness uses a one-second period and timeout with a failure threshold of five; readiness uses a one-second period and timeout with a failure threshold of one. The tutorial explains that a liveness-triggered restart reloads the large model, which is why its example waits for five consecutive failures. These are settings for that sample deployment, not universal recommendations: validate the endpoint’s response time and the recovery behavior of your own model server before adopting values.
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Diagnose the failure before changing thresholds
The title alone does not reveal whether the issue was a liveness restart, readiness removal, a startup window that expired, a slow health endpoint, a dependency outage, or a real process hang. Work out which action occurred before tuning probes.
- Inspect Pod events and restart counts. Check the Pod description and events to determine whether a startup, liveness, or readiness probe failed, and whether the container restarted or was merely removed from Service traffic. Check any load-balancer health status separately; it can affect routing without proving that Kubernetes restarted the container.
- Inspect the health endpoint’s work. Determine whether it performs expensive operations or waits on a remote service. A liveness check should not turn an external dependency failure into a restart decision; a readiness check tied to shared external connectivity can withdraw many otherwise healthy Pods at once.
- Compare probe timing with observed latency. Measure how long the endpoint takes to respond under model load, then compare it with the configured timeout, period, and failure threshold. A timeout shorter than normal response time can create repeated failures even when the process is making progress.
- Check the startup budget against actual initialization. Establish how long model download and loading take in the environment that failed. If initialization can outlast the startup probe’s allowed window, the configuration may treat a slow but functioning startup as a failure.
- Separate routing recovery from process recovery. Use readiness to stop sending new work to a temporarily unavailable instance. Reserve liveness-triggered restarts for evidence that the process needs one, such as a genuine deadlock or unrecoverable state.
Tune probe policy to the workload, not a template
Probe settings are operational policy, not constants. Review each design across five questions: what action follows failure (restart or traffic removal); what state the endpoint tests (process responsiveness, ability to serve the model, or dependency reachability); how sensitive the period, timeout, and thresholds are; whether startup protection covers the full initialization budget; and how expensive recovery is, including re-downloading or reloading a large model.
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There is no general incident-rate figure established for how often poorly scoped checks cause AI-serving incidents. The useful lesson is not to avoid probes, but to align each probe’s meaning and failure action with the condition it can reliably detect.
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