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dd-audit-cost-spike-investigation

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by datadog-labs · part of datadog-labs/agent-skills

Investigate a Datadog product usage or cost spike by correlating Usage Metering data (when/what spiked) with Audit Trail config changes (who changed what in the preceding window).

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🧩 One of 7 skills in the datadog-labs/agent-skills package — works on its own, and pairs well with its siblings.

This is the playbook your agent receives when the skill activates — you don't need to read it to use the skill, but it's here to audit before installing.

Audit Trail: Cost / Usage Spike Investigation

Identify what caused a Datadog usage spike by correlating billing data with configuration change history.

The causal chain is: someone changed something → that change increased data volume → usage spiked → cost went up. Usage Metering tells you when and what; Audit Trail tells you who made the change.

Scope Boundary

This skill identifies configuration changes that may have caused a spike. It does not identify which specific user or process submitted the data (e.g., which service sent the LLM spans). For per-submission attribution, use LLM Observability traces or APM instrumentation.

Investigation Workflow

Step 1 — Identify the spike window and product family

START=$(date -u -v-7d +"%Y-%m-%dT%H:%M:%SZ" 2>/dev/null || date -u -d "7 days ago" +"%Y-%m-%dT%H:%M:%SZ")
END=$(date -u +"%Y-%m-%dT%H:%M:%SZ")

curl -s -G "https://api.${DD_SITE}/api/v2/usage/hourly_usage" \
  -H "DD-API-KEY: ${DD_API_KEY}" \
  -H "DD-APPLICATION-KEY: ${DD_APP_KEY}" \
  --data-urlencode "filter[timestamp][start]=${START}" \
  --data-urlencode "filter[timestamp][end]=${END}" \
  --data-urlencode "filter[product_families]=all" \
  | jq '[.data[] | {
      timestamp: .attributes.timestamp,
      product: .attributes.product_family,
      measurements: [.attributes.measurements[] | {type: .usage_type, value: .value}]
    }]'

Product families with LLM/AI coverage: llm_observability, bits_ai, logs, apm

Step 2 — Pinpoint the spike

From Step 1, identify the hour/day where volume jumped. Note the timestamp as SPIKE_TIME.

Step 3 — Search Audit Trail for config changes in the 24h preceding the spike

pup audit-logs search \
  --query "@action:(created OR modified OR deleted)" \
  --from "SPIKE_TIME_MINUS_24H" \
  --to "SPIKE_TIME" \
  --limit 200 \
  -o json \
  | jq '[.data[] | {
      timestamp: .attributes.timestamp,
      user: .attributes.attributes.usr.email,
      actor_type: .attributes.attributes.evt.actor.type,
      action: .attributes.attributes.action,
      event_category: .attributes.attributes.evt.name,
      resource_type: .attributes.attributes.asset.type,
      resource_id: .attributes.attributes.asset.id
    }]'

Note: --from and --to accept ISO timestamps (e.g., 2026-05-01T14:00:00Z) or relative values (1h, 24h, 7d).

Step 4 — Narrow to product-relevant config changes

Filter to the audit categories most likely to affect the spiking product:

If this product spikedAdd to query
llm_observability@evt.name:(Integration OR APM OR "Log Management")
logs / indexed_logs@evt.name:"Log Management" @asset.type:(pipeline OR index OR exclusion_filter)
apm / indexed_spans@evt.name:APM @asset.type:(retention_filter OR sampling_rate)
rum@evt.name:RUM
metrics@evt.name:Metrics

Example for LLM Observability spike:

pup audit-logs search \
  --query "@evt.name:(Integration OR APM OR \"Log Management\") @action:(created OR modified)" \
  --from "SPIKE_TIME_MINUS_24H" \
  --to "SPIKE_TIME" \
  --limit 100 \
  -o json \
  | jq '[.data[] | {
      timestamp: .attributes.timestamp,
      user: .attributes.attributes.usr.email,
      action: .attributes.attributes.action,
      category: .attributes.attributes.evt.name,
      resource_type: .attributes.attributes.asset.type,
      resource_id: .attributes.attributes.asset.id
    }]'

Output Format

Usage spike detected:
  Product: <product_family>
  Spike time: <SPIKE_TIME>
  Volume: <baseline> → <spike_value> (<magnitude>×)

Configuration changes in 24h preceding spike:
  <timestamp> | <user_email> | <action> <resource_type> <resource_id> | <category>

Likely causal change: <most-proximate change matching the product family>

Confidence: HIGH (single clear change) / MEDIUM (multiple candidates) / LOW (no matching changes)

Next steps:
  - Confirm with <user_email> whether the change was intentional
  - If unintentional: revert <resource_id> and monitor volume
  - If intentional: update cost forecasts and alert thresholds

When No Causal Change Is Found

  1. The change may predate the 24h window — expand to 72h
  2. The increase may be from application-side instrumentation changes — check deploys
  3. The increase may be organic traffic growth — correlate with product launch or traffic event

References