Three separate Cloudflare incidents opened and closed within about 24 hours last week, and none of them shared a cause. A network hardware problem in Ashburn on the evening of September 17. Elevated errors for Durable Objects and several downstream services in APAC a few hours later. A Workers KV issue that took down parts of the dashboard the next morning. Every one of them was minor, every one was resolved within roughly 90 minutes, and every one had a different root cause in a different system.
If you were on call for a service that sits behind Cloudflare that night, you probably didn't experience it that way. You experienced three pages in one shift, and the first thing your brain did was ask what connects them.
Three Incidents, One Night
Here's the actual timeline, pulled from Cloudflare's own incident history:
18:29–19:58 UTC, Sept 17 — network performance issues in Ashburn, VA (IAD), attributed to a hardware fault.
23:37 UTC Sept 17 – 00:54 UTC Sept 18 — elevated errors for Durable Objects and downstream services (AI Search, D1, Workflows, Containers) specifically in the APAC region.
06:42–08:15 UTC, Sept 18 — a Workers KV problem affecting the Cloudflare dashboard.
Different data centers, different products, different hours of the day. The only thing they have in common is the vendor's name in the status page banner. But stack three status-page entries on top of each other in a Slack channel and they stop looking like three incidents — they start looking like one bad night at Cloudflare, and "Cloudflare is having a bad night" becomes the working theory for whatever your own service is doing.
The Reflex to Connect the Dots
This isn't a discipline failure. It's how on-call attention actually works. When a second alert fires while you're still mid-investigation on the first, checking for a shared cause is the cheap, obvious move — it might save you from duplicating work, and if there really is a shared upstream failure, missing it means fixing the same symptom twice. The instinct is correct often enough that nobody thinks to question it in the moment.
The problem is that the instinct doesn't come with an expiration condition. Nothing prompts you to stop assuming correlation once you've checked and found none. So the assumption just persists by default, quietly shaping how you read every subsequent signal for the rest of the incident.
Two Ways to Get This Wrong
Both directions cost you something different.
Assuming a shared cause that isn't there sends the whole team's attention toward one hypothesis. Someone starts building a timeline that stitches Ashburn's network fault to the APAC Durable Objects errors, looking for the dependency that ties them together — hours before, region before, deploy window before. There isn't one. Meanwhile the two incidents that actually needed separate owners and separate fixes both sit half-investigated, because everyone's staring at the same shared-cause whiteboard.
Assuming independence when there is a shared cause is the quieter failure, and it's the one that recurs. If two "unrelated" incidents both trace back to the same recent config change, the same overloaded upstream dependency, or the same deploy window, and nobody checks, you close both tickets, write two separate postmortems, and ship the same latent cause back into production twice. The Roundup on redundancy and failure domains covered exactly this pattern — systems that looked independently redundant but shared one physical or logical dependency underneath, which only showed up when both "independent" paths failed close together.
The Five-Minute Check Before You Merge Incidents
Before your team's mental model of the night collapses into "everything's connected" or "nothing's connected," it's worth spending five minutes making the comparison explicit instead of implicit. Four questions, asked out loud in the incident channel, before anyone commits to a theory:
1. Same service or component? Not "same vendor" — same actual system. 2. Same region or data center? 3. Same recent change — a deploy, a config push, a scaling event — in the window before both incidents started? 4. Shared upstream dependency, even one layer removed?
If the answer to all four is no, say so explicitly and split the incidents — separate owners, separate timelines, separate postmortems. If any answer is yes, that's the thread to pull before doing anything else. Either way, the team now has a stated reason for its theory instead of an inherited one, which matters more than which answer you land on. An unstated assumption is the thing that survives a shift handoff and quietly becomes "fact" three hours later.
This is also where tooling fragmentation makes the problem worse, not better. The piece on four monitoring tools showing four views of one incident covered how Geneos, Grafana, Splunk, and Slack can each tell a slightly different story about the same outage. Multiply that by three simultaneous incidents and everyone on the call is reasoning from a different partial picture, which makes the "are these connected" question harder to answer cleanly and easier to guess at instead.
One Incident's Factors vs. Three Incidents' Factors
It's worth being precise about what kind of multiplicity you're dealing with, because "multiple factors" and "multiple incidents" get conflated constantly. The Verica Open Incident Database's analysis of real postmortems found that the median incident involves 3.5 contributing factors, and incidents with five or more contributing factors take three times longer to resolve. That's a real, well-documented failure mode — a single incident where a config change, a missing alert, and a capacity gap all had to line up together.
That is a different problem from three status-page entries with three separate causes landing in the same shift. Treating a multi-incident night like it's one incident with several contributing factors sends you looking for the wrong kind of connection — you go hunting for the config change that ties Ashburn's hardware fault to a Workers KV bug, when the actual answer is that the vendor just had three ordinary Tuesdays back to back.
Read the Vendor's Blame, Not Just the Timestamp
None of this means ignore the possibility of a shared cause — the Roundup on outages caused by someone else's outage is a reminder that vendor incidents genuinely do cascade, and dismissing every pileup as coincidence is its own version of the same mistake. The fix isn't to default to "connected" or default to "unrelated." It's to make the check itself a habit, cheap enough to run every time three pages land in one shift, so the team's read on the night comes from four explicit questions instead of from however crowded the status page happened to look at 2 a.m.
On-call trust erodes either way you get this wrong — either from chasing a connection that wasted an hour, or from missing one that came back a week later wearing a different incident number. The fix costs five minutes and a habit. The alternative costs a shift's worth of attention spent solving the wrong problem, twice.