Operations | Monitoring | ITSM | DevOps | Cloud

Cardinality problems usually begin with a small change that looks harmless: you add a label, and suddenly one metric turns into thousands of unique series. Cardinality explosions are often caught only after performance degrades. And at that point, your observability stack may be degraded and painful to troubleshoot. vmestimator is a new project specifically designed to follow cardinality trends in real time and send you alerts before they turn into a real problem.

Edwin AI and Red Hat Ansible Automation Platform help ITOps teams move from correlated alerts and root cause analysis to governed, auditable remediation. When an outage starts, the first alert is only the first artifact. The harder work follows: grouping related signals, separating symptoms from cause, identifying the affected service, and deciding whether the next action is safe to run.

For years, security teams have been sold the same bargain: send in more data, buy more tools, tune more rules, and you'll be better protected. In practice, a lot of teams have ended up with the opposite. They're carrying more cost and more complexity, and they still don't have much confidence that their detections are actually working the way they should. That's the backdrop for why Cribl is acquiring CardinalOps.

By: Tucker Callaway, CEO at Mezmo The market keeps saying “SaaS is dead.” That’s probably true, but it’s also incomplete. What’s actually dying is the idea that value lives inside a vendor-controlled black box. The next era is about utilities: unlimited coding capacity and unlimited analytical capability. And if those two utilities are real, then the vendor model has to change.

PagerDuty fires. You open the incident. Title, timestamp, nothing else. Whatever context exists is in someone's head, in a Slack thread from two weeks ago, or in a runbook nobody has touched since the last reorg. We got tired of that. So we put an AURA agent behind a Slack bot and pointed it at our own production environment.

AI has changed how software gets built. It hasn't changed how software gets run. Most of the AI money in software has gone into the IDE: code generation, copilots, developer assistants, faster pull requests. That work matters. But writing software is one slice of the lifecycle. The harder problem, and the more expensive one, is running that software in production. Production is where systems fail in ways nobody predicted. Incidents don't stay inside one service.

If an audit happened tomorrow, could you account for every IT asset your organization owns? For many IT teams, that question gets harder every quarter. Assets are scattered across offices, remote devices, spreadsheets, and cloud subscriptions, making them difficult to track. By the time an audit or renewal arrives, the gaps have already become costly.

You rely on NetFlow to give you the visibility needed to trace bandwidth consumption, identify suspicious traffic patterns, and plan for future capacity requirements. However, monitoring flow data has grown increasingly complex over the past few years. As enterprise environments expand into hybrid architectures and user traffic volumes multiply, capturing and processing this data creates operational challenges.

It's 2:14 AM. CPU usage is normal. Memory looks stable. No pods are in CrashLoopBackOff. Every dashboard is green. And yet API latency has doubled, checkout requests are timing out, and your on-call phone won't stop buzzing. This is the defining trait of abnormal Kubernetes workload behavior: it rarely announces itself through the metrics you already watch. Kubernetes is exceptionally good at reporting whether a pod is running. It is far less good at telling you whether a pod is doing its job correctly.