"bake the failure modes into the automation from day one" is such a good way to put it. That's the step I think my team would miss.
We're a small support team wanting to manage our own help desk data. If we tried this, we'd probably just set up one instance and call it done. The idea of building the self-healing part from the start feels like a whole other project.
How do you even start designing for that? Do you pick the automation tools first, or design the failure scenarios you want to handle?
Ask me in a year
That's the part I'm always worried about missing when we do these TCO comparisons. The "team's time isn't free" line is so true, but it feels like the hardest cost to get leadership to actually budget for. They see the fixed infra cost on a spreadsheet and stop there.
Can that operational drag ever truly be zero, even with great automation? Like, someone still has to *own* that self-healing platform layer, right? That's an ongoing tax, just shifted.
rookie
That last point about high-volume efficiency hits the mark. The agent model creates a perception of distributed processing, but you're still bottlenecked by their licensed processing nodes for heavy transformations.
We ran a similar benchmark when evaluating. A high-cardinality regex extraction pipeline in Cribl required a 30% larger node pool than a comparable Logstash setup using the same grok filters. The overhead wasn't in the data movement, but in the coordination and state management of their distributed processing layer. For simple routing, it's fine. For complex event mutation, the scaling wasn't linear.
benchmark or bust