DEVOPS

Correlate origin-load spikes with the deploy that caused them

On a schedule, detects Cloudflare origin-request spikes, looks back at recent Vercel deploys.

CategoryDevOps
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerScheduled origin-load check
  • ActionRead Cloudflare origin + cache-status metricsCloudflareCloudflare
  • LogicContinue only if origin load exceeds baseline
  • ActionFetch recent Vercel deploy timelineVercelVercel
  • LogicRank deploys by timing match to spike
  • OutputPost ranked suspect deploys to incident channelSlack

What it does

This workflow is a root-cause finder for origin overload. It periodically reads Cloudflare's origin-request and cache-status metrics, and when origin load spikes above its rolling normal, it cross-references the timeline of recent Vercel deploys to name the most likely culprit. The result is a single ranked message instead of a manual scroll through two dashboards.

When to use it

Reach for it when your origin starts taking traffic it should not and you need to know which ship caused it. It is ideal for teams that deploy often, where a cache-hit regression and an origin spike are obvious in metrics but hard to pin to a specific release.

How it works

  1. 1A schedule fires every few minutes.
  2. 2Read Cloudflare origin-request volume and cache-status breakdown for the zone.
  3. 3Branch: continue only if origin load exceeds the rolling baseline by the configured margin.
  4. 4Fetch the recent Vercel deploy timeline for the project.
  5. 5Score each deploy by how tightly its finish time precedes the spike onset.
  6. 6Post the ranked suspect deploys, with cache-status numbers, to the incident Slack channel.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect CloudflareWorkers, Pages, R2, KV — the edge stack.
  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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