Operations | Monitoring | ITSM | DevOps | Cloud

If you’re running AI, ML, or data-intensive workloads on GPUs, monitoring their performance is critical. Overheating, under-utilization, or memory bottlenecks can cost you thousands in cloud bills and potential downtime. This guide walks you through collecting real-time GPU telemetry using nvidia-smi, sending it to Cribl Edge, routing it through Cribl Stream, and using Cribl Search to analyze the data—step by step.

Does sensitive data flowing through your network feel like a ticking time bomb? Well, it just might be. Legal mandates, security frameworks, and customer expectations have made the stakes higher than ever. One leaked spreadsheet of personally identifiable information (PII) can wipe out years of customer trust, rack up regulatory fines, and invite ransomware actors to your doorstep.

If you’ve ever found yourself staring at a cloud bill that could buy a small island or at least a very nice car, you're not alone. Believe me, at Cribl, we've had our share of those "molotov cocktail" bills that make our CFO, Zach, look like he's about to spontaneously combust. And yeah, a few F-bombs might have dropped from various senior leaders (myself included, I won't lie).

Let’s face it: managing global data infrastructure isn’t just hard, it’s “I-just-deployed-the-wrong-config-to-prod-again” hard. If you’re a Cribl Admin or Operator working across clouds, continents, and compliance regimes, your to-do list probably reads like a series of increasingly desperate Post-it notes. Sources. Destinations. Pipelines. TLS settings. Proxies. Dev, staging, prod. Repeat. Forever. But what if we told you there’s a better way?