Operations | Monitoring | ITSM | DevOps | Cloud

This post is also available in German and in French. Tracking environmental impact can be fragmented, time-consuming, and disconnected from operational data. Beyond simply checking ESG reporting boxes or making sure your company is CSRD compliant, actively monitoring environmental impact is the foundation for building an effective sustainability strategy. At Upsun, we know that measuring progress is the first step toward improvement.

Application delivery has accelerated over the last decade. Modern CI/CD pipelines, automated testing, and cloud infrastructure have already raised the baseline. Now AI-assisted coding tools are compressing timelines further still - developers are writing and shipping code faster than ever.

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.