Okay, I might be totally off base here, but I've been diving into our Cribl Stream trial for my team and something hit me. I was just so focused on getting our logs *out* of one system and *into* Splunk, that I didn't see the bigger picture at first.
Our rep kept mentioning "data routing" and "multiple destinations," and I finally get it. If you're just using Cribl as a fancy pipe to one place, you're probably not using its real power. It feels like buying a Swiss Army knife and only using the toothpick.
I think the value really clicks when you start sending the *same* stream of data to at least three different places. For example, we're now testing:
- Our main expensive analytics platform (like Splunk) gets the enriched, critical data.
- A cheaper cloud storage (like S3) gets a raw copy for compliance and replay.
- A real-time monitoring tool gets a filtered subset for specific alerting.
Before this, we were debating if the cost was worth it just for the Splunk optimization. But spreading the data out to serve different needs? That's where the math starts to make sense for us. The per-GB cost gets spread across more value, if that makes sense.
Am I thinking about this right? For those of you using Cribl in production, did you also find that the ROI only became clear once you started routing to multiple endpoints? I'd love to hear how others are structuring their data flows.
✌️ annie
You've hit on the critical economic model that justifies the platform. The "three destinations" idea isn't arbitrary, it's the practical threshold where you start to see a positive return on the compute overhead Cribl introduces. Your example of splitting between Splunk, S3, and a real-time monitor is the classic trifecta for cost optimization. However, your point about spreading the per-GB cost across more value is only half the equation. The real savings often come from the aggressive filtering and reduction you can now apply to that primary, expensive destination because you've satisfied compliance and replay needs elsewhere. The math fails if you're just cloning full-fidelity streams everywhere; the value multiplies when you're creating purpose-built, reduced streams for each target.