Operations | Monitoring | ITSM | DevOps | Cloud

In this two-part blog series, I give a detailed report-out on how our Honeycomb engineering team 2.5x-ed our throughput using AI without breaking everything or lowering our standards for quality. Part 1 explains how we did it and shows data about how that ramp-up happened. Part 2 shares what we learned.

There is a particular kind of incident every SRE team is familiar with. A common component of your stack, say your Redis database, starts misbehaving. Someone spends two hours tracing it back to a connection pool exhausted by a misconfigured client, the fix goes in, and everyone moves on, for today. The following Tuesday it happens again, and whoever is on call investigates it from scratch, because the person who solved it last week is asleep, on vacation, or working somewhere else now.

Mean time to resolution (MTTR) measures the average duration to restore regular operation for an application, service, or infrastructure component. It’s a key performance indicator (KPI) for IT incident management. To tie MTTR directly to customer satisfaction, you first need to understand how it affects service and application reliability and availability. From there, you can make informed decisions, operate efficiently, and provide a seamless customer experience.

For most IT leaders, multicloud didn't arrive as a decision. It arrived as a fait accompli. A team chose AWS for one workload. Azure came in through a Microsoft enterprise agreement. A SaaS acquisition brought its own cloud dependencies. A DR requirement pointed to a second region with a different provider. Nobody declared a multicloud strategy; the organization just became one. Today, 87% of organizations run a multicloud strategy, balancing an average of 2.6 public cloud providers simultaneously.

Kubernetes efficiency metrics now work with infrastructure filters.