Traditional observability measures latency, errors, throughput and cost. AI systems also need instrumentation for the behaviour of probabilistic components — including Models-as-a-Service, where infrastructure ownership sits with the provider but semantic risk remains with the system owner.
Cross-model disagreement can surface decisions that deserve investigation when independent models diverge.
Output instability can reveal sensitivity to repeated or perturbed inference.
Input-distribution novelty can identify shifts away from historically observed inputs.
Subpopulation monitoring can detect bias, drift and failure that aggregate metrics hide.
MaaS removes access to much of the model internals and training distribution. Monitoring therefore has to combine the signals that are available: system behaviour, disagreement, instability, input novelty, deterministic violations and sampled evaluation. The objective is not to manufacture certainty, but to make degradation and semantic risk visible enough to act on.
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