Many teams run Kubernetes clusters across on-premises environments, different cloud providers, or hybrid configurations. Managing and optimizing workloads in these diverse infrastructures presents several challenges, from management complexities to poor resource utilization.
Cast AI now extends its automation and cost optimization capabilities beyond the three major cloud providers (AWS, Azure, and Google Cloud Platform) to on-premises Kubernetes clusters and additional cloud providers like Red Hat OpenShift, Oracle Cloud, IBM Cloud, and Linode.
With features like automated workload rightsizing, bin-packing, and comprehensive cost monitoring, Cast AI enables DevOps and platform teams to improve efficiency, reduce costs, and enhance overall performance.
Let’s explore the most common issues teams face when running Kubernetes on-prem and how Cast AI addresses them via automation.
Challenges of managing Kubernetes on-prem
Organizations running Kubernetes on-premises or across multiple cloud providers face unique challenges that impact both cost and operational efficiency:
- Overprovisioning and idle resources – Organizations tend to overprovision resources without proper workload optimization, leading to waste, unnecessary OPEX increases, and higher energy consumption.
- Lack of automated workload rightsizing – Manual resource adjustments result in inefficient operations, forcing teams to spend valuable time managing workloads instead of focusing on innovation.
- Fragmented ecosystem – Tracking inefficiencies across multiple environments is difficult without uniform cost monitoring, making it difficult to enforce optimization strategies effectively.
- Complex infrastructure management – On-prem environments require additional hardware provisioning, maintenance, and updates oversight.
- Security and compliance – Managing security and ensuring compliance across physical infrastructure and hybrid setups demands rigorous attention, adding operational burdens.
- Maintenance and updates – Kubernetes clusters should be upgraded every three months. Phased upgrading is recommended, starting with development/test clusters, to avoid API incompatibilities.
Automation for Kubernetes on-prem: three use cases
Unified optimization across clusters
Organizations running Kubernetes across on-premises, multi-cloud, and hybrid environments often struggle with fragmented resource optimization solutions, leading to inconsistent performance, increased costs, and operational complexity.
Cast AI delivers a unified optimization platform that standardizes cost and performance management across all environments, whether on newer managed Kubernetes cloud platforms, bare metal, or private data centers. This ensures consistent optimization, reduces complexity, and enables teams to manage clusters efficiently at scale.
Automated workload rightsizing
On-premises environments lack the elasticity of public clouds, making resource efficiency crucial. Cast AI simultaneously applies Horizontal Pod Autoscaler (HPA) and Vertical Pod Autoscaler (VPA) policies, ensuring workloads automatically scale to match demand without overprovisioning.
Efficient resource utilization ensures CPU and memory allocations align with real-time requirements, eliminating guesswork. Dynamic adjustments enhance cost efficiency by reducing resource waste and optimizing energy consumption for on-prem hardware.
Learn more about Cast AI’s Workload Autoscaler
Cost monitoring and alerting
Effective cost management is often overlooked in on-prem setups. Dynamic resource allocation, lack of direct mapping between workloads and costs, and differences in pricing models between cloud and on-prem infrastructure make showback and chargeback challenging. Cast AI provides detailed cost reporting at organizational, cluster, and workload levels. Customizable dashboards enable better planning, while automated monitoring integrates with tools like Prometheus, Grafana, and ELK Stack to issue alerts when anomalies are detected.
Optimizing costs for Kubernetes on-prem with automated bin-packing
In multi-node Kubernetes environments, inefficient scheduling often leads to fragmentation and underutilized capacity. Cast AI’s bin-packing technology optimizes workload placement within nodes to maximize efficiency and minimize waste.
The video creation platform PlayPlay implemented Cast AI to optimize costs using bin-packing, among other solutions. Bin-packing improves on-prem Kubernetes resource management in three ways:
- Increased node utilization – The solution ensures workloads are packed efficiently, reducing the number of nodes required.
- Automated pod scheduling adjustments – It continuously reorganizes workloads to free up capacity without manual intervention.
- Cost and energy savings – Optimized resource allocation lowers energy consumption and extends the lifespan of on-prem hardware.
Optimizing costs for Kubernetes on-prem: the energy efficiency factor
Resource waste on-premises translates directly to higher energy consumption and increased hardware costs. Idle servers and racks in data centers consume energy continuously without generating any business value. This underutilization not only results in higher electricity costs and a larger carbon footprint but can also reduce hardware lifespan due to prolonged usage, all without yielding additional revenue or performance benefits.
Optimizing resource utilization and eliminating idle capacity are crucial for organizations to reduce energy waste, improve efficiency, and lower costs associated with on-premises infrastructure. This operational efficiency reduces costs and supports sustainability initiatives by decreasing energy usage.
Conclusion
Managing Kubernetes across on-premises and multi-cloud environments is complex and requires a suite of tools and processes. Cast AI eliminates this complexity by automating infrastructure management and improving visibility.
Automated workload management ensures that scaling and resource adjustments happen instantly, minimizing manual interventions. Real-time cost visibility empowers FinOps teams to make informed decisions and avoid wasteful spending.
With Cast AI’s expanded capabilities, organizations can bring automation-first Kubernetes optimization to any infrastructure. Whether managing on-prem Kubernetes clusters, deploying across hybrid environments, or leveraging alternative cloud providers, Cast AI ensures workloads run at peak efficiency with reduced costs and minimal manual effort.
