Operations | Monitoring | ITSM | DevOps | Cloud

Let’s say you want to make sure your website works as expected. You do not want to check if it just loads. You also want to check if important buttons or features are there and working. Oh, and you don’t want to just do it once. You want to keep an eye on this pretty much all the time. And, of course, you don’t want to keep checking manually if anything broke – you want to be notified, alerted when (not if) things break. You can do this by creating a Browser Monitor.

GitLab’s audit logs offer a goldmine of insights into user activity, project changes, and security events. Getting that data into Graylog for centralized analysis is easier than you might think—especially with the flexibility of our Raw HTTP input and Illuminate’s GitLab Spotlight Pack. In this two-part guide, we’ll walk you through how to get it done, from wiring up GitLab’s Audit Event Streaming to visualizing enriched events in a purpose-built dashboard.

You’ve built something amazing. Your services are scaling, your users are happy, and your team is shipping code like never before. Then the cloud bill arrives, and one line item makes your eyes water: observability. That Datadog invoice feels less like a utility bill and more like a ransom note. It’s a modern engineering paradox. The tools that give you sight into your complex systems are the same ones that can blind you with runaway costs.

Platform teams are struggling with observability noise, bloated storage costs, and lack of clarity during incidents. Most teams capture everything all the time, leading to expensive, overwhelming, and often unnecessary data volumes. In Telemetry for Modern Apps, Mezmo teamed up with Checkly to demonstrate how synthetic monitoring triggers and responsive telemetry pipelines can help reduce costs while maintaining the context needed during incidents.

Ever look at your to-do list at the end of the day and realize it’s grown longer, not shorter? We get it—there’s always more to do and never enough time. But if you’re a Sumo Logic user, reading this blog will be a win for your day because we’re giving you six ways to slash the time you spend on tasks in your platform.

As organizations rely more on complex IT systems and cloud-based services, keeping everything running smoothly — and reliably — has become a top priority. That’s where IT service performance monitoring comes in, giving teams the visibility they need to make sure systems stay healthy and responsive. By tracking a range of technical and user-focused metrics, businesses can quickly identify and address issues before they impact operations or end users.

When I spoke at AWS London earlier this year, I had the chance to discuss something that more and more teams are starting to feel: traditional observability doesn’t cut it for AI systems. In AI, “Is it running?” is no longer enough. We have to ask, “Is it right?” When I delivered that line, I saw the heads nodding. Everyone’s excited to build with LLMs, but when it comes to actually monitoring them in production? That’s where things fall apart.