Operations | Monitoring | ITSM | DevOps | Cloud

Interface counters tell you a port is busy. Bytes in, bytes out, errors, drops. That’s enough to know a link is saturated, but not enough to know which conversations are saturating it, which devices are involved, or how a problem propagates across your network. For that you’ve traditionally needed dedicated network performance monitoring tools, usually expensive, usually a separate console from the rest of your monitoring.

Agentic coding tools like Claude Code can write, refactor, and debug across an entire codebase, but by default they read code as plain text, the way grep does. The Language Server Protocol (LSP) changes that: it’s the same code-intelligence layer an IDE uses, and wiring it into an agent lets it read code by meaning instead of by string match. The bigger the codebase, the more a wrong guess about a symbol costs, and the more that structural view pays off.

The core of ROI is visibility. If you can clearly see … 1. What it costs to produce the thing you make, and 2. How much money it makes you … then calculating ROI is easy. But with AI, as with the cloud before it, getting that visibility is extremely challenging. Why? Because the cost data associated with each is inherently chaotic.

CloudZero Explorer now tracks your query history and lets you save up to 12 configurations and 10 favorite dimensions, so your most-used cost analyses are one click away.

The Preservation Hold Library silently inflates SharePoint storage. Replace it with Squirrel Recycle Bin Capture for compliant retention in customer-owned Azure.

Today, we’re launching Kubex support for the KAI Scheduler and automated GPU sharing for inference workloads. As AI inference moves into production, platform teams are being asked to serve more models, support more teams, and control GPU costs at the same time. But many inference workloads do not need an entire GPU all the time. When teams reserve full GPUs or oversized GPU fractions to stay safe, expensive capacity can sit idle across the cluster.

Eight buyer questions for SharePoint archiving 2026. Compares Microsoft 365 Archive, AvePoint Opus, Archive360 and Squirrel on data, Copilot, labels.

Machine learning models are not like other software artifacts. A single fine-tuned LLM can weigh 70 GB. A model family may share 95% of its weights across dozens of variants. When hundreds of developers, training jobs, and GPU clusters all need the same model at the same time, the infrastructure underneath needs to be built for it.

AI adoption has accelerated faster than most organizations expected. What started with chatbots has quickly evolved into AI systems capable of making decisions across enterprise environments, with the promise of faster service and more efficient teams. But many organizations are discovering an unexpected challenge: as AI usage expands, costs become harder to predict. Most AI platforms operate on token-based pricing models.