Operations | Monitoring | ITSM | DevOps | Cloud

When you operate a restaurant, the last thing you want to do is shut your doors and turn away guests and staff because of some technology failure. And if you’re the one providing that tech, it’s your job to make sure that doesn’t happen. “For us, observability is about a lot more than just dashboards and alerts.

I'll start this, the third and final entry in my series on APM and Observability, which was originally inspired by my contribution to an APMdigest article, by once again pointing out that APM tools can be built with observability in mind. Many are, in fact. And the ones that aren’t don’t turn into a different type of tool. In my experience, it's more that there's a difference of mindset.

We've just released Refinery 3.0, a performance-focused update which significantly improves Refinery's CPU and memory efficiency. Refinery has a big job: it performs dynamic, consistent tail-based sampling that maintains proportions across key fields, adjusts to changes in throughput, and reports accurate sampling rates.

Companies invest millions of dollars in observability platforms, yet they often still struggle to get application monitoring right. This is because most organizations focus on the technology, while neglecting the business. In this article, we’ll show you how to combine business requirements with technological needs. As the CTO of Logz.io, these are based on my experience working with global companies on their application observability needs.

Four months ago, we announced our vision of AI-first observability. Today, we’re not just talking about the future, we’re shipping it. This week marks a significant milestone with several major product announcements that demonstrate our continued momentum as the industry’s leading AI-first observability platform.

When an incident lands in your lap, you’ll often start with a lot of questions: Why is latency so high? What’s causing this outage? How much money are we losing at this very moment? The uncertainty—and the pressure to quickly find answers—has always been one of the more nerve wracking parts of being an on-call engineer, but it doesn’t have to be that way any more.

Organizations are adding GenAI to their current and future architectures and product roadmaps, requiring Ops teams to ensure LLMs are accurate, fast, secure and cost-efficient. LLM observability tools directly addresses these needs, helping identify and prevent common LLM errors and issues: LLM observability provides the telemetry data for this analysis. LLM observability tools trace requests end-to-end, evaluate outputs, and correlate quality with latency, cost, prompts, tools, and data sources.

In the previous post, What is OpenTelemetry?, we went over the What, Why, and the How of OpenTelemetry. We also went over the telemetry data lifecycle (data generation à collection à storage à usage) and how telemetry data (MELT) could be put to use to troubleshoot a representative web application scenario.