Our Kubernetes consulting services help clients migrate to, and optimize performance for, on-premise Kubernetes. Unlike cloud-managed services, on-premise Kubernetes require specific skills that aren't easy to source. We help our clients navigate the platform complexities. Our approach facilitates and accelerates knowledge transfer. And we ensure Kubernetes security best practices are understood and implemented at any infrastructure layer. You may also request an independent audit for your workloads and take a second opinion on your existing setup.
Our exclusive auditing and compliance services–led by CKA, CKAD, CKS and Linux Platform Engineers –are followed by a community of over 1,100 technology professionals, validating the niche yet impactful nature of our work in the Kubernetes ecosystem.
Kubernetes security is paramount to ensuring your clusters and workloads are protected from threats. Our comprehensive security services focus on safeguarding your Kubernetes environment with best-in-class practices and tools. From securing your containerized applications to implementing network policies, our experts ensure your infrastructure remains robust and secure.
Container security in Kubernetes environments demands a layered approach, starting with the container image itself. Employing minimal base images, regularly scanning for vulnerabilities using tools like Trivy or Clair, and enforcing strict image signing policies are crucial first steps.
Implement least privilege principles by running containers with non-root users and leveraging Kubernetes' security context features to limit capabilities.
Furthermore, maintain a secure registry, whether self-hosted or cloud-based, and ensure proper access controls. Regularly update images and Kubernetes components to patch known vulnerabilities, and consider using image pull secrets to restrict image access.
Utilize Network Policies to segment application workloads and restrict communication between pods. Employ service meshes like Istio or Linkerd to enforce mutual TLS (mTLS) for inter-service communication and provide advanced traffic management capabilities.
Implement robust ingress and egress controls, using web application firewalls (WAFs) and API gateways to filter malicious traffic.
Regularly audit network configurations and monitor network traffic for anomalies using tools like Cilium or Calico. Secure the Kubernetes API server by enabling RBAC, limiting access to sensitive resources, and regularly rotating certificates.
Role-Based Access Control (RBAC) is a method of regulating access to computer or network resources based on the roles of individual users within an organization. Instead of assigning permissions directly to users, RBAC grants permissions to roles, and then assigns users to those roles. This simplifies access management, as changes to permissions only require modifications to the roles, not individual user accounts. It also promotes the principle of least privilege, ensuring users only have the necessary permissions to perform their job functions.
In Kubernetes, RBAC governs access to the API server, which controls all aspects of the cluster. It defines who can perform what actions on which resources. Roles define sets of permissions, while RoleBindings and ClusterRoleBindings associate those roles with users, groups, or service accounts. Roles are namespace-scoped, meaning they apply only to resources within a single namespace, while ClusterRoles are cluster-wide. This allows for fine-grained control over access to Kubernetes resources, ensuring security and compliance.
RBAC in Kubernetes leverages subjects (users, groups, service accounts), roles (or ClusterRoles), and bindings (RoleBindings or ClusterRoleBindings). When a user attempts to perform an action, the API server checks if the user's subject is bound to a role that grants the necessary permissions. This model allows administrators to easily manage permissions for large numbers of users and resources, enhancing security and simplifying administrative overhead. Properly configured RBAC is crucial for protecting sensitive data and maintaining a secure Kubernetes environment.
We analyze your existing setup, identify bottlenecks, and implement solutions to enhance the efficiency and speed of your containerized applications.
Proper resource allocation ensures that workloads get the necessary CPU and memory without excessive over-provisioning or underutilization. Defining requests and limits in pod specifications prevents resource contention and maintains cluster stability. Requests guarantee a minimum allocation, while limits prevent excessive consumption.
Node autoscaling and cluster autoscaler mechanisms help dynamically adjust the number of running nodes based on demand. This prevents overloading certain nodes while keeping costs in check. Horizontal Pod Autoscaler (HPA) can also be employed to scale application instances based on CPU and memory usage.
Effective namespace-based resource quotas and policies can prevent any single workload from monopolizing resources. Combined with priority classes, they ensure critical applications receive sufficient resources, avoiding performance bottlenecks.
Kubernetes provides built-in load balancing mechanisms such as Service objects (ClusterIP, NodePort, LoadBalancer) and Ingress controllers to distribute traffic efficiently. Properly configuring these components helps balance workload distribution and prevents hotspots.
For internal communication, kube-proxy manages traffic routing between services, but using a service mesh like Istio or Linkerd offers finer control, including intelligent routing, retries, and circuit breaking. These advanced features optimize microservices interactions and improve resilience.
In multi-cluster or hybrid-cloud environments, global load balancers ensure seamless traffic distribution across clusters. Solutions like Kubernetes Gateway API or external DNS integration enable better cross-cluster communication, reducing latency and improving failover strategies.
Monitoring Kubernetes clusters is crucial for detecting performance issues and preventing outages. Tools like Prometheus, Grafana, and Datadog provide visibility into key performance metrics such as CPU, memory usage, network traffic, and pod health.
Using Kubernetes Metrics Server, cluster administrators can track real-time resource consumption and adjust scaling strategies accordingly. Persistent logging solutions like Fluentd, Loki, or ELK Stack help correlate logs with performance anomalies for deeper analysis.
Implementing proactive alerting and anomaly detection ensures rapid response to potential issues. Tools like Alertmanager (part of Prometheus) can trigger alerts based on threshold breaches, allowing teams to react before service degradation occurs.
Kubernetes offers unparalleled scalability and flexibility, but without careful cost management, expenses can spiral out of control. By focusing on resource efficiency, autoscaling, and monitoring, organizations can reduce costs while maintaining performance. Leveraging FinOps (Financial Operations) principles ensures that cloud spending aligns with business objectives, providing visibility, accountability, and efficiency.
Optimizing resource allocation is key to reducing waste in Kubernetes environments. Underutilized CPU and memory can lead to excessive cloud spending, while overprovisioning adds unnecessary costs. Using resource requests and limits effectively ensures that workloads receive only the resources they need, preventing over-allocation.
Right-sizing workloads through vertical and horizontal pod autoscaling ensures that applications run efficiently without consuming unnecessary resources. Additionally, tools like KubeCost and AWS Cost Explorer help analyze cluster utilization and suggest optimizations based on workload patterns.
From a FinOps perspective, cost efficiency requires continuous collaboration between engineering, finance, and operations teams. By setting cost budgets, analyzing trends, and enforcing best practices like rightsizing nodes and leveraging spot instances, organizations can maximize resource efficiency without compromising performance.
Autoscaling is essential for balancing performance with cost. Kubernetes provides Horizontal Pod Autoscaler (HPA) to dynamically adjust the number of running pods based on CPU, memory, or custom metrics. This ensures that workloads scale up during peak times and scale down to minimize costs when demand drops.
For infrastructure-level scaling, Cluster Autoscaler optimizes node provisioning by adding or removing nodes based on demand. To further optimize costs, cloud providers offer Spot Instances (AWS), Preemptible VMs (GCP), and Azure Spot VMs, which allow for lower-cost compute resources with the trade-off of potential interruptions.
A FinOps approach to autoscaling involves monitoring cloud spend, setting up cost allocation tags, and enforcing guardrails on autoscaling configurations. By aligning autoscaling policies with budgetary constraints and forecasting future demand, organizations can prevent over-provisioning while maintaining service reliability.
Effective monitoring is crucial for identifying inefficiencies and optimizing Kubernetes costs. Tools like Prometheus, Grafana, and OpenCost provide visibility into cluster utilization, helping teams understand which workloads are consuming the most resources. Cloud-native monitoring solutions such as AWS Cost and Usage Reports, GCP Cost Management, and Azure Cost Analysis enable deeper insights into spending patterns.
By implementing FinOps best practices, teams can track cost trends, allocate expenses to specific teams or projects, and optimize spending over time. Setting up alerts and budgets ensures that unexpected cost spikes are addressed proactively. Additionally, adopting chargeback and showback models helps promote cost accountability across teams, encouraging responsible cloud usage.
Cost optimization in Kubernetes isn’t just about reducing spend—it’s about making smart, data-driven decisions that align with business goals. By integrating FinOps principles, organizations can build a sustainable cost management strategy while maintaining high-performance Kubernetes environments.
We set up and manage Kubernetes clusters ensuring high performance, security, and scalability. Our experts provide end-to-end support to help you leverage the full potential of Kubernetes across environments.
Kubernetes’ true power lies in its ability to integrate with various tools and services. Whether it's CI/CD pipelines, service meshes, or cloud provider services, we help integrate the right tools to enhance automation, security, and observability.