ENGINEERING

Fast-Burn Spike Escalation to PagerDuty

On a Datadog fast-burn alert webhook, this workflow confirms the SLO is burning faster than the configured multiplier and pages the on-call engineer through PagerDuty…

CategoryEngineering
Enginesim
Difficultyintermediate
Triggerwebhook
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerDatadog fast-burn webhookHTTP webhook
  • ActionRe-fetch authoritative SLO stateDatadogDatadog
  • LogicConfirm multiplier and compute hours left
  • ActionTrigger PagerDuty incidentPagerDutyPagerDuty
  • OutputPost incident link to SlackSlack

What it does

It catches the dangerous case the hourly forecast can miss: a sudden fast burn that will drain the budget in hours, not weeks. It validates the spike against the current SLO state and escalates straight to a human via PagerDuty with the time-to-exhaustion already calculated.

When to use it

Use it for tier-1 services where a fast-burn event needs a page now, not a Slack message someone reads in the morning. Pair it with the hourly forecaster: that one watches slow leaks, this one watches blowouts.

How it works

  1. 1A Datadog fast-burn monitor fires an HTTP webhook into the workflow.
  2. 2It re-queries the Datadog SLO to get authoritative remaining budget and current burn rate, avoiding action on a stale alert payload.
  3. 3A logic step computes hours-to-exhaustion and confirms the burn multiplier exceeds the escalation threshold.
  4. 4If confirmed, it triggers a PagerDuty incident with severity scaled to how little time remains.
  5. 5It posts the incident link to the team Slack channel for visibility.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect HTTP webhookTrigger any URL on agent actions.
  2. 2
    Connect DatadogMetrics, traces, log search.
  3. 3
    Connect PagerDutyIncidents, on-call, escalations.
  4. 4
    Connect SlackChannels, DMs, threads, mentions.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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