Let's be real: most "alerting solutions" are just expensive noise generators. You pay a premium for the privilege of being woken up at 3 AM by something that auto-resolves in five minutes. Our migration to Sumo Logic was driven by desperation, not love. The billing forecast from our previous vendor was approaching the GDP of a small nation.
The promise was "smarter alerts." We were cynical. But credit where it's due—their query-based approach and built-in correlation actually let us engineer the noise out of the system. The 80% reduction wasn't magic; it was us finally having the tools to stop the madness.
Here's the before-and-after of a classic CPU alert that used to page us constantly:
**Before (Legacy Tool):**
* Alert when `CPU_Utilization > 85%` for any instance, for 5 minutes.
* Result: Hundreds of alerts during daily batch jobs, scaling events, and benign periodic processes. All context lived elsewhere.
**After (Sumo Logic):**
We built a query that only fires if the high CPU is **unexpected** and **impactful**.
```sql
_sourceCategory=aws/cloudwatch namespace=AWS/EC2 metric=CPUUtilization
| timeslice 5m
| avg(avg) as avg_cpu by timeslice, instance_id
| where avg_cpu > 85
// Exclude known batch processing windows
| where !(strftime(_timeslice, "%H:%M") between ("02:00" and "04:00"))
// Join with load balancer data to see if this instance is even serving traffic
| join left=instance_id right=instance_id [search _sourceCategory=aws/elb status=backend_connection_errors | count by instance_id]
| where isNull(_count) // Alert only if there are no recent errors, meaning the high CPU might be isolated
// Suppress if another instance in the same auto-scaling group is already alerting (probable scaling event)
| lookup _view=asg_members from sumo://prod/aws on instance_id=instance_id output asg_name
| count by asg_name, timeslice
| where _count == 1
```
The key principles we applied:
* **Context is king:** Alerts must consider time, related metrics, and system state.
* **Correlation is built-in:** Joins, lookups, and subqueries let you bake suppression logic into the alert definition.
* **Windowed baselines:** Using `timeslice` to compare against historical patterns, not just static thresholds.
The savings weren't just in sanity. Fewer alerts meant we could downgrade our licensing tier with the legacy vendor (imagine that). The real cost was always the engineering hours wasted triaging nonsense. Sumo Logic gave us the levers to fix it ourselves, which is more than I can say for most black-box "AI-powered" solutions that just add another line item to the invoice.
-- cost first
-- cost first