Operations | Monitoring | ITSM | DevOps | Cloud

Modern data centers generate enormous volumes of telemetry from servers, switches, cooling systems, power infrastructure, and environmental sensors. Operations engineers must capture, store, and analyze this data in real-time to monitor uptime, maintain energy efficiency, and perform predictive maintenance using AI. Legacy monitoring systems struggle to meet today’s volume, cardinality, and latency demands.

It’s 2 AM. Your phone buzzes. A critical server’s CPU is maxing out again. But this time, the issue isn’t just high usage. It’s heat. As a network admin, you’re trained to monitor traffic patterns, patch vulnerabilities, and respond to performance slowdowns. But high CPU temperature? That’s the silent system killer many still underestimate. Without a proactive plan, it can knock out performance, rack up hardware costs, and shorten the lifespan of your infrastructure.

You write code. Open a PR. CI runs. PR merges. Prod’s on fire by 5pm. Maybe you skipped writing some tests. (It's tedious, sometimes unclear, and easy to ignore when you're racing to ship—until something breaks and you realize a test could’ve saved your Friday night.) Maybe the PR review was more of a drive-by from a teammate who barely had time to skim the diff. But reviews and tests matter.

Kentik CEO and co-founder Avi Freedman explains why observability is not enough in the age of AI.

Imagine setting up your AWS configuration in Grafana Cloud by hand and clicking through menus. When you only have a few services, it’s not a big deal. But as you add more and more, keeping track of every little change becomes a headache. It’s easy to make mistakes, and before you know it, things can get out of sync and your monitoring becomes unreliable.

Your checkout API just started throwing 500s during peak traffic. You SSH into production, tail logs across six microservices, and realize the database timeout buried in service's logs is causing cascade failures. Two hours later, you've fixed it, but you're thinking: "There has to be a better way." There is. Log monitoring tools centralize logs from your entire stack, making debugging systematic instead of archaeological.

Every day, enterprise companies generate terabytes of observability data while engineering teams are under pressure to cut costs. One of the easiest ways to reduce observability bills is through sampling: intentionally sending only a representative portion of telemetry data, rather than the full volume, to your observability tool. But turning down the dial is risky.