DEVOPS

Auto-rollback Vercel when error budget burn spikes after release

Continuously checks the Sentry error-budget burn rate against the latest Vercel release and, if burn exceeds the fast-burn limit, promotes the previous deployment back…

CategoryDevOps
Enginesim
Difficultyadvanced
Triggerschedule
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerSchedule: every few minutes
  • ActionPull error rate and compute SLO burn rate from SentrySentrySentry
  • ActionFetch latest and previous production deploys from VercelVercelVercel
  • LogicBranch on fast-burn threshold breach
  • ActionPromote previous Vercel deployment back to productionVercelVercel
  • OutputPost rollback summary to SlackSlack

What it does

Guards your SLO error budget against bad releases. On a short schedule it computes the current burn rate from Sentry error volume, attributes it to the most recent Vercel production release, and if the fast-burn threshold is breached it triggers a rollback by re-promoting the prior known-good deployment, then reports what happened.

When to use it

Use it when a regression must not be allowed to drain a month's error budget in minutes and you want the system to revert a bad deploy automatically rather than waiting for a human to wake up.

How it works

  1. 1A schedule runs every few minutes during active hours.
  2. 2The flow pulls the current event/error rate from Sentry and computes the burn rate against the SLO budget.
  3. 3It fetches the latest production deployment and the previous good one from Vercel.
  4. 4A branch checks whether burn exceeds the fast-burn limit.
  5. 5If breached, it promotes the previous Vercel deployment back to production.
  6. 6It posts a rollback summary with the burn rate and reverted release to Slack.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect SentryErrors, performance, releases.
  2. 2
    Connect VercelDeploys, runtime logs, analytics.
  3. 3
    Connect SlackChannels, DMs, threads, mentions.
  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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