Hi everyone. I'm currently evaluating Cribl for my team. We're looking to get a better handle on our observability data costs and routing.
I have a basic PoC setup, but I need to build a business case. Beyond just "it looks useful," what specific metrics should I track during my trial to prove a hard ROI? I'm thinking about things like data volume reduction pre- vs post-Cribl, or license cost savings for our downstream tools.
Any advice on what was most convincing in your own evaluations would be really helpful. Thanks in advance
Data volume reduction is a great starting point. Track the raw bytes ingested into Cribl versus what it sends to your expensive tools, like Splunk or Datadog. That's a direct cost save.
But don't forget the time savings. Can you measure the engineer hours saved on things like creating custom parsing or routing logs without Cribl? That operational overhead is real money too.
What about storage costs? If you're routing filtered data to cheaper, long-term storage, that's another metric to quantify. I'm curious, are you mainly trying to save on vendor licensing or internal resource time?
Everyone starts with data volume. It's an easy win. But don't mistake that for the whole picture.
The big miss is measuring the new costs you're taking on. Factor in the compute for running Cribl itself, and the operational overhead of managing yet another pipeline component. Their licensing model can get complex.
Track the delta in your support tickets too. If you're just moving complexity from your Splunk team to your new Cribl team, your real ROI is zero.
Read the contract
You're absolutely right that "the operational overhead of managing yet another pipeline component" is a huge factor. It's easy to get excited about the reduction line on a chart and miss the management line going up.
In my experience, that overhead can actually become a benefit if you centralize complexity. Instead of five teams building fragile parsing in five different tools, one small pipeline team handles it. But you have to staff and measure that new team's time as a real cost, like you said. If you just reassign someone and don't track their hours, the savings are imaginary.
The support ticket delta is a brilliant, practical metric. We also tracked the time to onboard a new data source. Without Cribl, it often meant Splunk admin tickets, index adjustments, and back-end work. With it, it became a pipeline configuration change. That shift in who does the work, and how long it takes, was the core of our operational ROI.
The right tool saves a thousand meetings.