MARKETING

Auto-rollback a Vercel deploy when real-user CWV spikes after release

Watches Datadog RUM for the 30 minutes after a Vercel production deploy, and if field LCP or INP on landing routes crosses threshold it rolls back to the previous deployment…

CategoryMarketing
Enginesim
Difficultyadvanced
Triggerwebhook
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerVercel production deploy succeededVercelVercel
  • ActionPoll Datadog RUM p75 LCP/INPDatadogDatadog
  • LogicDetect field regression vs baseline
  • ActionRe-promote previous deploymentVercelVercel
  • ActionPage on-callPagerDutyPagerDuty
  • OutputPost rollback summary to SlackSlack

What it does

Closes the loop between deploy and real-user performance. After a production deploy, it monitors Datadog Real User Monitoring for field LCP and INP on landing routes and, if the new release degrades the actual user experience, reverts the Vercel alias to the last good deployment automatically.

When to use it

Use it when lab checks pass but real-world regressions still slip through — third-party tags, device mix, or CDN behavior that only shows up in the field. Best for high-traffic landing pages where minutes of degraded LCP cost conversions.

How it works

  1. 1A Vercel production deployment-succeeded webhook starts the watch window.
  2. 2The flow polls Datadog RUM for p75 LCP and INP on the tracked routes over a rolling 30-minute window.
  3. 3A logic step checks whether the post-deploy p75 exceeds the pre-deploy baseline by more than the allowed margin.
  4. 4If it regresses, the flow calls the Vercel API to re-promote the previous deployment.
  5. 5It pages on-call through PagerDuty and drops a summary with the before/after numbers in Slack.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect VercelDeploys, runtime logs, analytics.
  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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