Operations | Monitoring | ITSM | DevOps | Cloud

Every leap forward in technology begins with a question that feels almost human in its curiosity. In this series, we’re examining those questions, the ones that reveal where intelligence meets intention. If data was the foundation of understanding in our first conversation, automation is where that understanding begins to act.

Stop guessing on AI hardware. This DevOps guide details when to use TPUs vs. GPUs for optimal performance, cost, and framework compatibility in MLOps.

Asset tracking software is becoming one of the highest return investments for organizations in 2026. With rising labor costs tightening budgets and increasing pressure for operational efficiency companies are searching for tools that reduce waste and maximize utilization. The demand for asset tracking management has grown rapidly because businesses are losing money through misplaced equipment delayed audits poor maintenance planning and underutilized resources.

Students can now learn what they need within the Elastic stack anytime. The Elastic Training team has shifted its on-demand training strategy from paid to free! Yes, you heard that right — complimentary on-demand training is now readily available to everyone. The Elastic Training team is continuously developing and releasing bite-sized training modules designed to align with Elastic solutions and highlight key features.

Icinga is designed to be a highly dynamic monitoring software that can monitor your setup, regardless of its architecture. While most setups are hierarchical and fit well into the master, satellites, and agents scheme with different zones, it is sometimes impractical or impossible to create one large Icinga 2 cluster. Imagine that you are responsible for only some hosts within another organization.

Welcome to December’s edition of CloudZero’s Cloud Economics Pulse — your monthly read on how cloud spend is shifting across providers, services, and AI workloads. No surprises here — November continued the quiet reshaping trend we’ve seen all year. Compute softened, data layers grew, and AI/ML hit its highest share yet. AWS extended its lead, Azure and GCP nudged upward, and the emerging “AI layer” of providers continued to take shape.

A few months back, a backend team at a fast-growing SaaS company shipped what seemed like a harmless feature. Just a simple request validation layer. No new service. No major dependencies. No architectural shock. Yet two months later, their cloud costs had climbed 38% without any significant increase in traffic, storage, or compute load. What they’d missed was that the validation layer triggered a fan-out pattern.

Python is deeply embedded in modern infrastructure, but many organizations continue to run outdated Python across critical systems. Sticking with older runtimes may seem harmless, but it quickly piles up technical debt as teams spend more time maintaining fragile code and applying workarounds. Over time, that debt translates into a high financial drain.

Engineering organizations depend on clear team ownership to maintain reliable services and move quickly. But as codebases expand and teams shift, answering basic questions—Who owns this service? Who should be paged in an incident? Are teams meeting operational standards?—becomes harder.

APIs rarely fail in isolation. They fail under load, during token refresh, when a dependent service slows down, or when a multi-step workflow breaks halfway through. And yet most engineers still test and monitor APIs using mock endpoints that behave nothing like the real thing.