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Discover our latest articles about cloud-native technologies, Kubernetes, DevOps, and modern software development. From practical tutorials to in-depth analyses.
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Stay up to date with our latest articles about cloud-native technologies, Kubernetes, and DevOps.
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Many IT managers in medium-sized businesses feel secure because they "do backups." However, in a serious incident—such as a massive cloud provider outage, a ransomware attack, or a human error in the root config—they realize: A backup is not a recovery plan.
In the IT world of 2026, sustainability is no longer just a marketing buzzword. With the expansion of EU reporting obligations (CSRD), IT decision-makers face a new challenge: they must not only estimate but accurately document the carbon footprint of their digital infrastructure.
When an AI model leaves the training phase, the real challenge begins: productive inference operation. Serving a Large Language Model (LLM) in a standard container is inefficient. Latencies are too high, and GPU utilization is often poor because traditional web servers are not built for the sequential nature of token generation.
In a Retrieval Augmented Generation (RAG) architecture, the vector database (Vector DB) is the core component. It provides the Large Language Model (LLM) with context from your enterprise data. However, while traditional databases are primarily optimized for disk I/O, vector databases like **Qdrant, Weaviate, or Milvus** impose entirely new demands on your Kubernetes infrastructure.
Anyone operating traditional microservices knows: metrics, logs, and traces are the lifeline. However, conventional monitoring approaches hit their limits with AI workloads. A CPU utilization of 10% tells us nothing about whether the response quality of a language model is currently dropping or if the vector search is inefficient.
Europe likes to see itself as a global guardian of data protection and fundamental rights. GDPR, NIS2, AI Act – the regulatory claim is high, the rhetoric confident. In operational reality, however, a different picture emerges: personal data of European citizens and companies is systematically outsourced to infrastructures lying outside European legal and control spheres. Not illegal, but politically shortsighted. Not out of necessity, but out of convenience.
The introduction of Artificial Intelligence in small and medium-sized enterprises has opened a new security front. When we train LLMs or build RAG systems (Retrieval Augmented Generation), we move massive amounts of sensitive data through our Kubernetes cluster—often directly onto powerful GPU nodes.
Integrating an NVIDIA H100 or A100 into your cluster today quickly reveals that the classic 1-to-1 allocation (one pod reserves an entire GPU) often results in massive capital waste in a production environment. While training LLMs fully utilizes the hardware, GPUs often idle at 10% utilization during inference operations or in development environments.
The excitement around Large Language Models (LLMs) and generative AI has brought a fundamental question back to IT departments: How do we scale Machine Learning (ML) workloads without creating parallel shadow IT?
In the past two years, "Internal Developer Platform" has become a buzzword. However, while Gartner praises the IDP as a remedy for developers' cognitive overload, many internal projects end up as rigid abstraction layers that create more problems than they solve.
Polycrate CLI 0.29.5 addresses a critical bug: Endpoints with wildcard hostnames (*.example.com) can now be synchronized with the API.
Polycrate API 0.11.3 fixes the K8sCluster Workspace filter: UUIDs are now correctly recognized. Prerequisite for Polycrate CLI 0.29.4.
Polycrate CLI 0.29.4 addresses critical operator bugs: Cluster API sync with UUID filter, TLS port synchronization, and Node RBAC for finalizer management.
Polycrate CLI 0.29.3 introduces Host SSH-Agent Auto-Mount, branch-aware Git operations, and operator fixes for accepted_status_codes and wildcard hostnames.
Polycrate API 0.11.1 temporarily disables the automatic Alert Notification Loop. Note notifications and manual notifications remain active.
When discussing the shift to Cloud-Native and Kubernetes, we often focus on architecture, providers, and costs. However, the most critical variable in this equation is not the tech stack—it's your team.
For critical applications, "down-time" is no longer just a technical issue for medium-sized businesses, but a direct business risk. However, the traditional response to high availability (HA) has been: Double infrastructure = double costs. A model that breaks many budgets.
We don't need to explain that FinOps is the answer to uncontrolled cloud spending. The challenge for IT decision-makers in medium-sized businesses today lies elsewhere: How can cost responsibility and technical scalability be intertwined so that the cloud doesn't become a bottomless pit?
Kubernetes has long been the standard when it comes to scalable and highly available software platforms. Anyone wanting to operate Kubernetes in the cloud sooner or later ends up at **AWS EKS (Elastic Kubernetes Service)** – one of the best-known managed Kubernetes offerings worldwide.
The decision by the state government of Schleswig-Holstein to consistently switch its administration to open source software is more than a political signal. It is a real, technically demanding transformation of a complex IT landscape – under full load, with around 60,000 employees, ongoing judicial and administrative operations, and clear strategic goals. This is precisely why this step is exemplary.
Digital sovereignty has long been part of every public sector digital strategy. However, the Sovereignty Barometer of public IT by next:public shows how large the gap between aspiration and reality is. The study provides resilient figures – and they paint a clear picture of structural dependency.
Current reports of massive data leakage from self-hosted Owncloud, Nextcloud, and ShareFile instances are technically unspectacular – and that is exactly what makes them so problematic. There was no zero-day, no compromised encryption, no architectural flaw in the software. Access was gained using valid user accounts. In some cases, with credentials that were years old.
Almost every modern company is working on an AI strategy today. Whether it's Large Language Models (LLMs), image recognition in quality control, or predictive analytics, the demand for computing power is enormous. However, while algorithms are becoming increasingly precise, IT departments face a new, physical challenge: GPUs (graphics processing units) are expensive, hard to come by, and their management differs fundamentally from traditional IT infrastructure.
"The cloud grows with your needs." This promise is both a blessing and a curse. For growing businesses, cloud scalability is essential to keep up with increasing user numbers and data volumes. However, in practice, rapid growth is often followed by shock: the monthly bills from hyperscalers rise faster than revenue.
