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Stay up to date with our latest articles about cloud-native technologies, Kubernetes, and DevOps.
1219 posts
In the industry, a fundamental architectural question arises: Should AI make decisions directly at the machine (**Edge**) or should the data be sent to a central system for deeper analysis (**Cloud**)?
In the realm of IT infrastructure, few things are as costly as a modern NVIDIA GPU doing nothing. An H100 or A100 instance with major hyperscalers often costs as much per hour as an entire office team consumes in coffee. When data scientists forget to shut down their instances after training, or when clusters idle while reserving expensive resources, costs can skyrocket within days.
In software development, Git is the "Source of Truth." When something goes wrong, you check the commit history. In the world of artificial intelligence, that's not enough. A model is not just code; it's a combination of **code, data, and hyperparameters**.
In a pilot project, many things are forgiven in AI. If an anomaly prediction takes two seconds, it's not the end of the world. But in industrial manufacturing - such as monitoring high-speed presses or robotic arms - time is not a relative concept, but a strict contractual parameter (SLA).
Public IT procurement in Germany has long been characterized by a structural contradiction. Politically, digital sovereignty was demanded, but in practice, proprietary solutions were preferred. Not out of conviction, but due to legal uncertainty.
In many data science teams, the workday begins with frustration: A shared notebook won't run because a library is missing. A model trained on colleague A's machine yields different results on colleague B's server. And onboarding new team members takes days until all CUDA drivers, Python venv environments, and paths are correctly configured.
In industrial AI, such as predictive maintenance for sensor data analysis software, data streams are the lifeblood. Thousands of sensors provide measurements every second that need to be filtered, aggregated, and passed on to inference models. **Apache Kafka** has established itself as the heart of this pipeline.
In many machine learning teams, an unwritten rule prevails: first come, first served. Whoever starts the first training job in the morning occupies the GPU—often for the entire day. The remaining data scientists wait, switch to slow CPU instances, or book expensive shadow IT in the public cloud.
In the world of Artificial Intelligence, there's a phenomenon we often refer to as the "Wall of Confusion." On one side is the data science team developing brilliant models in Jupyter Notebooks. On the other side is the ops team responsible for stability, latency, and SLAs in production.
An open letter from 25 European cloud and digital companies reveals what European digital policy has been avoiding for years: The term "digital sovereignty" is consistently used without its prerequisites being met.
Since 2021, the French government has been pursuing a well-structured approach to the digital transformation of the state with the action plan "Free Software and Digital Commons." Open Source is not treated as a technical detail but as a strategic tool to strengthen state capabilities and technological independence.
Polycrate vs. plain Ansible: The Honest Direct Comparison
Polycrate MCP: Hub, docs, and schemas for AI assistants – stdio, tools, and wiring Cursor, Claude, and more correctly explained
Enterprise Automation: Building, Versioning, and Sharing Polycrate Blocks Within Teams
Managing Raspberry Pi and Edge Nodes with Polycrate and Ansible in IoT and Edge Computing
Workspace encryption with Polycrate: GDPR-compliant secrets management without external tooling
The recent warnings from CISA and Amazon about active attacks on Cisco FMC, Microsoft SharePoint, and Zimbra initially appear to be a routine process in IT security: vulnerabilities are identified, assessed, published – and then patched.
Automate compliance requirements: Policy as Code with Ansible and Polycrate
Orchestrating Helm Charts with Ansible and Polycrate: more control, more reproducibility
SSH sessions and kubectl debugging directly from the Polycrate workspace
Digital sovereignty is often discussed in abstract terms, but it can be technically delineated quite clearly: the key is what systems are bound to. Once applications depend on specific infrastructures, proprietary APIs, or non-standardized operating models, a coupling arises that can only be resolved later with significant effort. In practice, this means that many architectures are formally portable but are not actually moved.
It's often not the visible features that determine the stability of modern platforms, but the inconspicuous systems in the background—those components that must function reliably without attracting attention. One such system is the focus of a recent post from the Kubernetes Blog: the **Kubernetes Image Promoter**—and its comprehensive, deliberately "invisible" redevelopment.
The discussion around AI infrastructure is noticeably shifting: away from mere model questions, towards the real challenge of how AI can be integrated into existing platforms in a controlled, secure, and efficient manner. A recent post from the Kubernetes Blog on the new **AI Gateway Working Group** provides an insightful impetus for this – and makes it clear that the Cloud-Native world is already preparing for the next generation of architecture.
CloudFest is one of the few events where the term "industry meeting" is not an exaggeration. This is where the layer of IT that builds, operates, and sells infrastructure comes together. Anyone who wants to understand where hosting, cloud, and platform economy are heading cannot miss this event.
