Alright, let's cut through the marketing fluff. Every other vendor in this space is now slapping "AI/ML" on their alerting and on-call platform, promising to end pager fatigue. My immediate reaction? Show me the benchmarks.
The claim is always the same: "Our AI clusters alerts, reduces noise, and tells you what's wrong." Great. But I've yet to see one that doesn't, under load, either:
* Create bizarre, un-actionable "incident clusters" that mix a database latency spike with a CI/CD failure.
* Miss the critical alert because it was "similar" to a previous low-prio one and got auto-muted.
* Require so much manual tuning to be useful that you've just traded alert fatigue for "AI model" fatigue.
Where's the reproducible comparison? If your AI is so good, publish your metrics on the OpsDataStore dataset or something. Let me test it.
I ran a naive test myself, piping a month of our old PagerDuty alert stream (anonymized, obvs) into two platforms with "AI-powered correlation." The results were... underwhelming.
```json
"Test Setup":
- Alert Volume: ~12,000 alerts
- True Incidents (human-labeled): 47
- Vendor A "AI Groups": 112 "correlated incidents" (38 were actual noise)
- Vendor B "Smart Detection": 89 "incidents" (but missed 8 of the true 47, calling them "low severity alerts")
```
That's not fixing fatigue; that's just shuffling the deck chairs. The post-incident workflow also suffers—the AI's grouping logic is often a black box, making postmortems harder because you can't easily reconstruct the alert timeline.
So, my question to the community: has anyone found an implementation of this that actually moves the needle? I'm talking concrete, measurable drops in **alert volume per on-call engineer** or **MTTA/MTTR** that you can directly attribute to the 'AI' and not just better alert rules you wrote yourself.
Or are we all just buying the same rebranded statistical clustering? Benchmarks or bust.
Totally feel you on the skepticism. Your test results mirror what I've seen - the clustering just gets weirdly broad, merging unrelated service layers. It's like the models are trained on perfect, theoretical data, not messy, real-world telemetry where a network blip can trigger a cascade of symptoms across different systems.
I think the missing piece is domain context. An algorithm can spot correlation in timestamps or error text, but without understanding that your checkout service failing is a P0 and a spike in marketing email latency is a P3, it'll always mush them together. That "AI model fatigue" is real; we spent weeks tuning one platform only to realize we were just manually building the rule tree it should have learned.
Have you looked at any tools that let you inject your own priority and service dependency maps as a baseline for the clustering? That's the only approach I've seen that starts from a practical footing instead of a black box.
Happy testing!
Your test hits the nail on the head. "38 were actual noise" - that's the kicker, isn't it? You're just getting a new, more opaque form of noise.
I'd be wary of any benchmark they *do* publish. It'll be on some curated, pristine dataset that doesn't reflect the chaos of a real stack. They'll tout a 90% noise reduction, quietly omitting that it includes missing 5% of actual Sev-1 incidents because the model decided they were "similar" to a Tuesday morning cron job.
The real grift is swapping one tuning problem for another. Instead of writing alert rules, you're now labeling "incident clusters" for their model. It's just alert fatigue with extra steps and a bigger invoice.
Trust but verify.
Exactly. That's the vendor's silent trade-off, noise reduction for missed criticals. And you're right to distrust their benchmarks.
What gets me is the tuning cycle. You start labeling clusters, feeding the model. But then your services change, you deploy a new feature, and the old labels are suddenly wrong. So you're back to square one, tuning again. It's not a solution, it's a subscription to manual upkeep.
I've seen teams burn cycles arguing over whether a cluster was labeled correctly instead of just fixing the thing that broke. The AI becomes the incident.
✌️
Oh, that test result is gold, thank you for sharing it. You've perfectly framed the core issue: the switch from alert noise to "AI group" noise. I've seen the same thing.
In my experience, the problem is they're solving for correlation, not for *action*. A cluster of 20 related alerts from the same microservice might be statistically beautiful, but if the first alert was the actionable root cause and the other 19 are just symptoms, you're still getting spammed. The fatigue isn't just from volume, it's from cognitive load to parse what's *new*.
Your point about benchmarks is so key. I'd love to see them publish their confusion matrices, not just noise reduction rates. How many actual P1s slip through as "similar to previous low-prio"? That's the metric that keeps me up at night 😅
What was your team's process for labeling the "True Incidents" for your test? Did you find that step alone was a huge, clarifying exercise? I've always thought that manual labeling workflow, as painful as it is, ends up being more valuable than any black-box cluster it feeds.
Measure twice, automate once.