Guardrail Metrics: Distribution Examples

Pass/fail breakdown per guardrail

Counts grouped by guardrail and outcome, useful for spotting guardrails that fail most often:

json={
    "startTs": "2026-04-21T00:00:00.000Z",
    "endTs": "2026-04-22T00:00:00.000Z",
    "datasource": "guardrailMetrics",
    "type": "distribution",
    "aggregations": [
        {"type": "count", "column": "guardrailName"}
    ],
    "groupBy": ["guardrailName", "guardrailResult"]
}

Latency percentiles on output-scope guardrails

p50, p90, and p99 grouped by guardrail, restricted to output-scope evaluations:

json={
    "startTs": "2026-04-21T00:00:00.000Z",
    "endTs": "2026-04-22T00:00:00.000Z",
    "datasource": "guardrailMetrics",
    "type": "distribution",
    "aggregations": [
        {"type": "p50", "column": "latencyMs"},
        {"type": "p90", "column": "latencyMs"},
        {"type": "p99", "column": "latencyMs"}
    ],
    "groupBy": ["guardrailName"],
    "filters": [
        {"fieldName": "appliedOnEntityScope", "operator": "IN", "value": ["output"]}
    ]
}

Slow guardrail evaluations

Volume and average latency of evaluations slower than 500 ms, grouped by guardrail:

json={
    "startTs": "2026-04-21T00:00:00.000Z",
    "endTs": "2026-04-22T00:00:00.000Z",
    "datasource": "guardrailMetrics",
    "type": "distribution",
    "aggregations": [
        {"type": "count", "column": "guardrailName"},
        {"type": "avg", "column": "latencyMs"}
    ],
    "groupBy": ["guardrailName"],
    "filters": [
        {"fieldName": "latencyMs", "operator": "GREATER_THAN", "value": 500}
    ]
}

Failures only

Restrict to failed evaluations, grouped by guardrail and scope:

json={
    "startTs": "2026-04-21T00:00:00.000Z",
    "endTs": "2026-04-22T00:00:00.000Z",
    "datasource": "guardrailMetrics",
    "type": "distribution",
    "aggregations": [
        {"type": "count", "column": "guardrailName"}
    ],
    "groupBy": ["guardrailName", "appliedOnEntityScope"],
    "filters": [
        {"fieldName": "guardrailResult", "operator": "IN", "value": ["fail"]}
    ]
}

Group by team and metadata environment

Counts by team and a custom metadata key, restricted via team array filter:

json={
    "startTs": "2026-04-21T00:00:00.000Z",
    "endTs": "2026-04-22T00:00:00.000Z",
    "datasource": "guardrailMetrics",
    "type": "distribution",
    "aggregations": [
        {"type": "count", "column": "guardrailName"}
    ],
    "groupBy": ["team", "metadata.environment"],
    "filters": [
        {"fieldName": "team", "operator": "ARRAY_HAS_ANY", "value": ["team-alpha", "team-beta"]}
    ]
}

Distinct guardrails per scope

How many unique guardrails ran on input vs output:

json={
    "startTs": "2026-04-21T00:00:00.000Z",
    "endTs": "2026-04-22T00:00:00.000Z",
    "datasource": "guardrailMetrics",
    "type": "distribution",
    "aggregations": [
        {"type": "countDistinct", "column": "guardrailName"}
    ],
    "groupBy": ["appliedOnEntityScope"]
}

Restrict to specific guardrails

Use IN on guardrailName to focus on a subset:

json={
    "startTs": "2026-04-21T00:00:00.000Z",
    "endTs": "2026-04-22T00:00:00.000Z",
    "datasource": "guardrailMetrics",
    "type": "distribution",
    "aggregations": [
        {"type": "count", "column": "guardrailName"},
        {"type": "p99", "column": "latencyMs"}
    ],
    "groupBy": ["guardrailName", "guardrailResult"],
    "filters": [
        {"fieldName": "guardrailName", "operator": "IN", "value": ["pii-detector", "toxicity-filter"]}
    ]
}

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Guardrail Metrics: Distribution Examples

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