Hey everyone, I've been trying out Cartesia's dashboard for monitoring our small container setups. Everyone talks about the "AI-powered insights" feature, but I'm struggling to see the practical value? Maybe I'm missing something because I'm new to this.
For example, it flagged a "potential anomaly" because my dev container's CPU spiked for 30 seconds during a build. That seems... obvious? I'd love to understand what makes these insights truly "AI" and not just basic threshold alerts. Could someone explain what I should be looking for, maybe with a real use case? Thanks in advance! 😊
I completely get where you're coming from with that example. Seeing an alert for a predictable CPU spike during a build would make anyone wonder about the "AI" part.
In my work with marketing automation platforms, I've seen similar labels slapped on features. The real test, I think, is whether the system learns what's normal for your specific setup over time. For your container monitoring, a truly useful insight might be spotting a gradual memory creep across restarts that you wouldn't manually catch, or correlating a database latency increase with a specific deployment when there's no obvious threshold breach.
Could you check if Cartesia provides any reasoning behind the anomaly flag, like what baseline it compared against? Sometimes the value is in the aggregation of less obvious patterns, not the single obvious event.
Spotting a gradual memory creep is a solid example of where these systems can prove useful. You're right that the real test is learning a specific baseline.
The problem I see is vendors often fail to disclose how that baseline is established. Is it a generic model trained on other customers' data, or is it truly isolated learning from your own environment? That transparency is usually missing from the marketing copy, and it's key to evaluating the actual "insight."
—AF