DEVOPS

Auto-investigate and roll back cost-spiking production deploys

When Datadog detects an edge-function cost anomaly after a production deploy, an agent correlates the spike to the deploy, decides whether to roll back via the Vercel API.

CategoryDevOps
Enginepaperclip
Difficultyadvanced
Triggerwebhook
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerDatadog cost-anomaly monitor firesDatadogDatadog
  • ActionCorrelate spike to recent Vercel deployVercelVercel
  • LogicAgent decides rollback vs hold
  • ActionRoll back to prior deploy if warrantedVercelVercel
  • ActionWrite incident summary to NotionNotionNotion
  • OutputPost decision handoff to SlackSlack

What it does

This is the production safety net for cost. When Datadog fires a cost-anomaly alert, an agent gathers context: which deploy preceded the spike, which functions drove it, and how far over budget the run-rate is now. It then decides whether the spike warrants an automatic rollback to the previous Vercel deployment, executes it if so, and documents the whole reasoning trail.

When to use it

Use it when an unbudgeted cost spike in production is an incident-grade event and waiting for a human to wake up is too slow. The agent handles the triage and reversible action; humans review the writeup.

How it works

  1. 1A Datadog cost-anomaly monitor webhook fires.
  2. 2An action queries Vercel for recent deployments and correlates the spike timing to a specific release.
  3. 3The agent reasons over the metrics and deploy diff to decide rollback vs hold.
  4. 4If rollback is warranted, it promotes the prior deployment through the Vercel API.
  5. 5It writes an incident summary to Notion and posts a Slack handoff with the decision and rationale.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect DatadogMetrics, traces, log search.
  2. 2
    Connect VercelDeploys, runtime logs, analytics.
  3. 3
    Connect NotionPages, databases, comments.
  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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