Operations | Monitoring | ITSM | DevOps | Cloud

This is a guest post from Adam Atreides. SQL Prompt's March releases focused on Prompt AI improvements and SSMS 22.4 compatibility.

Let me paint a picture. Your company has rolled out Claude or ChatGPT as the standard AI tool. You've connected MCPs to Stripe, your HRIS, Datadog, your CRM, and Slack. A senior engineer set this up because they needed to answer hard cross-system questions and it works beautifully. Now a marketing intern sits down, opens the same LLM harness with the same MCP config, and types "show me revenue by customer for the last 12 months." They get it.

One of the first decisions teams face when adopting Konstruct is whether to run the control plane themselves or have it managed for them. While this can look like a simple deployment choice, it is really a question of operational responsibility, control, and how your platform needs to evolve over time. Both models exist to solve the same underlying problem: providing a consistent, GitOps-driven platform across teams and environments.

In preparation for launching CertKit last week, I browsed the websites of a lot of related cybersecurity services. I don’t really understand what any of them do, but apparently, “trust” is a thing that can be sold now.

Kosli gives your organization a complete picture of software delivery - every build, scan, deployment, and compliance event tracked. Until now that picture was most useful to the people managing governance. However, developers shipping code had to ask someone else what versions of their code were running, how long it was taking to get to production, or what their deployment frequency was. Repositories change that.

The early era for AI was defined by experimentation, standing up isolated environments, and finding the first practical use cases. Today, the conversation is different. Enterprises are no longer asking whether AI matters. They are asking how to scale it sustainably, securely, and economically. That shift is giving rise to the AI factory: a repeatable, governed, production-ready environment where data scientists, platform teams, and application teams can build, train, deploy, and operate AI at scale.

TL;DR: Most Kubernetes clusters waste GPU compute through over-provisioned pod requests and suboptimal node selection. This guide covers 10 tools that fix this across four layers: resource lifecycle (Kubex, ScaleOps, Cast.ai), hardware partitioning (GPU Operator, MIG, time-slicing), inference serving (Triton, KServe), and observability (DCGM Exporter, NFD). For most teams, the biggest gains are at the resource lifecycle layer: no model changes required.

Custom dashboards in Netdata have always let you pull charts together on-the-fly into a single view. That’s useful, but it’s also limited. In practice, when you’re running an incident or reviewing a service, you don’t just want charts. You want to see the output of top alongside your CPU metrics. You want slow query logs next to your database latency charts.

The regulatory landscape governing UK sovereign cloud security has shifted more dramatically in the past 12 months than in the preceding decade. New legislation, tightened procurement frameworks, and an intensifying cyber threat environment are collectively raising the compliance floor for organizations running cloud workloads in the UK.