Custom AI Models

Inference health for the models your organization deploys — latency, failures, and the thresholds that decide what counts as broken
Models

12

+2
Inferences (24h)

4.8M

+11%
Failure rate

0.42%

+0.09%
p95 latency

318ms

-24ms

Registered models

Every model an organization has registered, scored against its own thresholds rather than a global default

ModelTaskRequests (24h)p95FailuresStatus
fraud-scorer

v4 · sagemaker

Classification2.1M84ms0.02%Healthy
risk-ranker

v2 · sagemaker

Ranking910K142ms0.11%Healthy
demand-forecast

v9 · vertex

Regression318K1.2s1.84%Degraded
doc-extractor

v1 · self-hosted

Extraction486K640ms0.31%Healthy
image-tagger

v3 · self-hosted

Vision92K3.4s6.20%Down
churn-predictor

v7 · azure ml

Classification204K96ms0.08%Healthy

No matching results

Thresholds

A model is marked degraded or down by its own limits. Raise them for a batch model that is allowed to be slow; tighten them for anything in a request path.

ms
%
%

Recent failures

ModelErrorWhen
image-taggerCUDA out of memory3m ago
image-taggerInference timeout5m ago
demand-forecastFeature null in payload18m ago
doc-extractorUnsupported MIME type41m ago

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