DEVOPS

Page the owning service team when projected month-end spend breaches its budget

Hourly it projects each service's month-end spend from BigQuery run-rate, and for any service tracking to blow past its budget it pages the owning team via PagerDuty and opens…

CategoryDevOps
Enginesim
Difficultyadvanced
Triggerschedule
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerHourly schedule
  • ActionProject month-end spend per serviceGoogle BigQueryBigQuery
  • LogicKeep services projected to breach budget
  • ActionPage owning team's escalation policyPagerDutyPagerDuty
  • OutputOpen GitLab issue with burn-down detailGitLabGitLab

What it does

Projects where each service's monthly cloud spend will land based on current run-rate, compares that projection to the service's budget, and escalates only the services on track to breach. Breaches page the owning on-call team in PagerDuty and open a tracking issue in GitLab so the overrun has both an immediate responder and a paper trail.

When to use it

Use it when teams have explicit monthly cloud budgets and a late-month surprise is unacceptable. Best for orgs that want proactive, owner-targeted escalation rather than a reactive end-of-month review.

How it works

  1. 1An hourly schedule triggers the projection run.
  2. 2A BigQuery query computes month-to-date spend per service and projects month-end via run-rate.
  3. 3A logic step compares each projection to the service's budget and keeps only those projected to breach by a meaningful margin.
  4. 4For each breach, an action triggers a PagerDuty incident routed to the owning service's escalation policy.
  5. 5An output step opens a GitLab issue in the owning repo with the projection, budget, and contributing cost lines, linked to the incident.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect BigQueryDatasets, queries, schemas.
  2. 2
    Connect PagerDutyIncidents, on-call, escalations.
  3. 3
    Connect GitLabRepos, MRs, pipelines, registry.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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