Kubernetes Development for Automated Delivery Processes
As Kubernetes experts, we develop automated delivery processes and scalable container solutions for modern cloud-native applications, serving clients across Northern Germany and the entire DACH region.
Kubernetes Development for Automated Delivery Processes Below you will find use cases, services and answers to common questions.
Our Kubernetes Services
Comprehensive container orchestration and cloud-native solutions for your infrastructure
Cluster Setup & Management
Professional setup and management of Kubernetes clusters on-premise or in the cloud (AWS, Azure, GCP).
Cloud-Native Apps
Development and migration of applications to cloud-native architectures with microservices and containers.
CI/CD Pipelines
Automated build, test and deployment pipelines with GitLab CI, Jenkins or GitHub Actions.
Security & Compliance
Implementation of security best practices, RBAC, network policies and compliance requirements.
Monitoring & Logging
Comprehensive monitoring with Prometheus, Grafana and centralized logging with ELK stack or Loki.
Auto-Scaling & HA
Configuration of Horizontal Pod Autoscaler, Cluster Autoscaler and high-availability setups.
Our Kubernetes Ecosystem
Proven tools and technologies for robust container infrastructures
Kubernetes
Container orchestration
Docker
Container runtime
Helm
Package manager
Istio
Service mesh
Prometheus
Monitoring
Grafana
Visualization
ArgoCD
GitOps
Traefik
Ingress controller
Advantages of Kubernetes
Our Kubernetes Process
Requirements Analysis
Analysis of your infrastructure and applications
Cluster Design
Planning the cluster architecture and resources
Implementation
Kubernetes setup and integration
Migration
Step-by-step migration of existing workloads
Training
Training your team in Kubernetes
Support
Ongoing support and optimization
Ready for Container Orchestration?
Let us modernize and scale your infrastructure with Kubernetes together.
Kubernetes in Practice: Orchestrating Containerized Workloads
Managing Kubernetes clusters in production requires more than deploying pods. We design node pools with proper taints and tolerations, configure resource requests and limits based on actual usage metrics, and implement namespace-level quotas to prevent noisy-neighbor problems. Our cluster architectures separate system workloads from application workloads, ensuring that monitoring stacks and ingress controllers never compete with business-critical services for compute resources.
Helm charts bring repeatability to Kubernetes deployments, but poorly structured charts create maintenance nightmares. We build modular Helm libraries with environment-specific value overlays, integrate chart testing into CI pipelines with helm-unittest, and use ArgoCD for GitOps-driven deployments that guarantee cluster state matches the Git repository. Service mesh integration with Istio or Linkerd adds mutual TLS, traffic splitting, and circuit breaking without modifying application code.
Auto-scaling in Kubernetes operates at multiple layers. We configure Horizontal Pod Autoscalers based on custom Prometheus metrics — not just CPU — to scale workloads based on queue depth, request latency, or business KPIs. Cluster Autoscaler provisions and deprovisions nodes within minutes, while Vertical Pod Autoscaler right-sizes resource requests over time. This layered approach keeps infrastructure costs aligned with actual demand across all environments.