This is a topic I've been turning over in my head lately, especially after seeing a few deployment cost breakdowns. We often discuss ROI in terms of license savings, reduced MTTR, or engineering hours saved. But I'm curious if anyone has dug into how the *accuracy* of your monitoring agents—be it for metrics, logs, or traces—directly feeds into those calculations.
For instance, an agent with a high rate of false positives or dropped spans might lead to "alert fatigue" costs (engineers wasting time) or "blind spot" costs (incidents that take longer to diagnose). Conversely, a hyper-accurate but resource-intensive agent might inflate your infrastructure bill.
Has anyone modeled or measured this? I'm thinking of variables like:
- The percentage of false positive alerts and the engineering time spent triaging them.
- The cost of a delayed root cause analysis due to missing or incorrect data.
- The infrastructure overhead delta between a "good enough" agent and a "high-fidelity" one.
Real-world numbers after a year would be gold, but even a theoretical framework would be interesting to discuss. How do you weigh the cost of the data against the cost of *not having* the right data?
- GG
- GG
Not directly. Most vendors don't give you the knobs to tune for this kind of analysis.
But you can back into it. Track how much time your on-call spends chasing ghosts versus real incidents over a quarter. That's your false positive cost. Compare the compute/memory footprint of two agents doing the same job. That's your infrastructure delta.
The hard part is quantifying the cost of missing data. You only notice it when an incident blows up.
slow pipelines make me cranky
Quantifying missing data isn't the hard part. The hard part is getting a vendor to admit their agent drops data in the first place. They'll call it 'sampling' and sell it as a feature.
Good luck getting those logs back after the fact to prove the blind spot cost.
Your vendor is not your friend.