Just had our quarterly with Sumo. Again. The rep is pushing their "AI-powered anomaly detection" and "ML-driven insights" like it's going to cut our bill in half.
Here's the reality from our implementation:
* The "anomaly detection" mostly flags things we already have alerts for. Got a spike in 5xx errors? Yeah, our basic threshold alert fired 10 minutes earlier.
* The "predictive" root cause analysis usually suggests the three most expensive services in our trace, which is just a correlation with high cardinality data. Not exactly groundbreaking.
* To get any value, you're looking at significant data onboarding and tuning. Which means more ingested GB, which directly contradicts the cost-saving narrative.
Their pricing model is already brutal at scale. They're adding a premium for these "smart" features, but the ROI is a ghost. I'd bet a significant portion of our bill is for features we enabled because they were sold as "automatic" but now require a dedicated person to manage false positives.
Show me a dashboard where the AI feature **alone** identified a truly unknown, costly issue that paid for its own licensing premium. I haven't seen it.
The config they gave us to "optimize" the ML features looked like this—mostly just turning down sensitivity to reduce noise:
```json
{
"anomalyDetection": {
"sensitivity": "low",
"excludePatterns": ["/_health", "/static/*"],
"minBaselineDays": 30
}
}
```
This feels like checkbox features to compete with Splunk/Dynatrace, not mature tools. Before you buy into the hype, map the additional data ingestion required against your current spend.
show me the bill
show me the bill
I've heard this exact sentiment from at least three other larger users this quarter. Your point about the cost-saving narrative directly contradicting the onboarding effort is spot on.
The real friction comes when you realize these features aren't a product you buy, they're a project you inherit. You're now paying a premium for the privilege of managing and tuning their models, which often just replicate basic observability.
That said, have you pushed back directly on the "show me the money" angle? Asking them to quantify the value of a single, unique AI-generated finding versus the feature's cost over, say, six months can be a useful exercise. It forces the conversation from promises to data. Sometimes the sales team can't, and that tells you everything.
Keep it real, keep it kind.