Why alert storms kill incident response
When 40 alerts fire for one root cause, your engineers aren't facing a monitoring problem. They're facing a signal quality problem.
Blog
From the Infrawatch team. We write about what we see in platform engineering environments — alert fatigue, OOMKill patterns, p99 tails, and the correlation layer most teams don't have yet.
When 40 alerts fire for one root cause, your engineers aren't facing a monitoring problem. They're facing a signal quality problem.
Your p50 looks fine. Your p95 is borderline. And 1% of your users are experiencing 10-second requests. Here's why that matters.
A pod dies quietly. Three services upstream see latency spikes. Your Slack explodes. Here's why OOMKills are the most undertracked incident signal in Kubernetes clusters.
Every team has more telemetry than they know what to do with. The gap isn't collection — it's the layer that links signals together.
The deployment happened 4 hours ago. The incident happened now. Most tools won't make that connection. Here's how to track it.
SRE teams optimize for individual service reliability. Platform engineering teams need to understand how 200 services fail together.
The correlation problem changes fundamentally when you cross 100 services. Here's what we learned building a system to handle it.
Most runbooks are written during calm reflection and read during panic. Here's how to close that gap.
MTTR measures how fast you resolved an incident. But how long did it take your team to understand what was happening? That gap is where the real cost lives.
OTel standardizes how you collect signals. It doesn't tell you what to do with them when three of them fire at the same time.
Engineers don't burn out because they care too much. They burn out because their tools make 3am worse than it needs to be.
Private beta to 3 paying customers, 7 major releases, one correlation engine rewrite. Here's what we shipped and what we learned.