Operations | Monitoring | ITSM | DevOps | Cloud

Engineering organizations spent the past decade learning to measure developer and team productivity. Measurement of the organization itself did not keep pace. Now that agents are responsible for writing most of the code across many engineering organizations, it’s more important than ever to have an effective methodology for measuring productivity.

A network monitoring platform can handle more than 1,000 devices, but the device total is only the starting point. A workable design must keep polling cycles on time, preserve visibility during failures, control notification volume, support the devices you own, and recover cleanly when the monitoring system itself has a problem. Crossing 1,000 monitored devices changes the job.

How many containers are running in your production environment right now that nobody can see inside? A vendored service, a compiled binary, an application whose build pipeline left with the developer who wrote it: each one runs, serves traffic, and reports nothing. Instrumenting those workloads means a code change, a rebuild, and a redeploy, and on these containers none of the three are available.

Splunk charges you in one of two ways: by how much data you send it each day, or by how much compute your searches and dashboards use. Security teams pay for both the platform and Splunk Enterprise Security, the app that turns Splunk into a SIEM, which is priced separately on top. This guide breaks down every part of a 2026 Splunk bill, works through a real, sourced pricing example, and lays out the ways to bring the number down, including the one lever many teams overlook.

Authored by Sven Delmas, VP of Research at Mezmo I wanted to know what Mezmo's new trace features feel like with real telemetry behind them, so I built the smallest honest rig I could: the OpenTelemetry demo application running in a local Kubernetes-in-Docker cluster on my machine, one collector, and one deliberately simple Mezmo pipeline.

Instead of building a separate role for every team, region, or department, admins can create a single role that automatically personalizes access for each user based on their SSO attributes. Someone moves teams or a new group gets created, and the new access takes effect at their next login with no CloudZero configuration. As AI spend grows, more companies are looking to give teams visibility into their own AI costs without exposing every individual’s usage across the org.

Anthropic, the company behind Claude, ran the most honest experiment in agentic AI: they gave a version of their own model $1,000, a small fridge in the office lunchroom, and one job. Run a shop. Make a profit. They named it Claudius. Claudius did not make a profit.

Microsoft Purview is an enterprise-scale platform for managing data governance across your whole cloud estate. It is not just about ensuring the integrity of data stored in SQL databases — it spans the whole spectrum of data storage including blob storage, document databases, email and AI frameworks. It has an extensive list of features for organising and monitoring your enterprise data. This includes.

When we introduced the Low Code Plugin (LCP) framework in February, the premise was simple: if a system has an API, you should be able to build a plugin for it — quickly, with minimal code, and in a way you can share with the community. The "AI-ready" part was deliberate. The framework was designed to work naturally with AI assistants, so the path from idea to working integration would be as short as possible. That design decision is now paying off.

When we launched our Governance Gap series, we set out to explore how the explosion of AI-assisted engineering changes the risk profile for modern software organizations. We looked at the rise of The New Software Creator and analyzed why deployment governance is what keeps teams safe when code production accelerates. We also mapped out the realities of security at scale and defined who owns governance accountability.