Kubernetes

Open source software to deploy, move, and manage containerized applications and services.

Proactively manage the containerized infrastructure by checking their health, restarting, and removing the failed and unresponsive ones.

Improve performance and reliability through load balancing to distribute traffic across multiple container instances, applications, or services.

Reduce cost, improve user experience, and ensure resource optimization by identifying available nodes and allocating containers onto those nodes.

Ensure increased protection, operational efficiency, and reduced operational risk by managing passwords, SSH keys, and other sensitive information.

Kubernetes, known as K8s, is a portable, extensible, and open-source platform for managing containerized workloads and services across private, public, and hybrid cloud environments.

With a large, rapidly growing ecosystem, its services, support, and tools are widely available. In addition, these containers run on top of a common shared operating system (OS) on host machines but are isolated from each other unless a user chooses to connect them.

Considering the available resources, with the help of Kubernetes, application developers, IT system administrators, and DevOps engineers can intelligently deploy, scale, maintain, schedule, and operate multiple application containers across clusters. Thus freeing up human resources to focus on other productive tasks.

Features

Vanilla Kubernetes

Access all the standard services and tools in the Kubernetes ecosystem using Catalyst Kubernetes.

Identity & access management

Obtain complete control access in Kubernetes using Catalyst Cloud account and role permissions.

Network policies

Enhance security through features like encryption and restrict network traffic between services running in pods.

Private clusters

Deploy clusters to a private network by default and only make them visible to the internet if you choose to do so.

Cluster resiliency

Improve the cluster’s ability to tolerate hardware failure by creating master and worker nodes in anti-affinity groups.

Use Cases

Machine Learning Operations

Data science teams can manage the entire machine learning lifecycle with Kubernetes, making it easier to scale and manage machine learning models in production.

Manage Hybrid Cloud and Multi-Cloud

Leverage multi-cloud capabilities to extend on-premises workloads, enhancing resiliency and service diversity.

Legacy Application
Modernization

Organizations looking to modernize legacy applications can containerize them and deploy them on Kubernetes. This approach enhances scalability and reliability, extending the life of legacy systems.

Extend Edge Computing

Integrate edge computing and IoT with data center app components for streamlined maintenance, deployment, and system management.