Red Hat OpenShift
Red Hat OpenShift is the leading enterprise Kubernetes platform for building, deploying, and managing applications across hybrid clouds. Discover features for AI, virtualization, CI/CD, and security.
Overview of Red Hat OpenShift
Red Hat OpenShift is an enterprise-grade Kubernetes container platform that enables organizations to develop, deploy, and manage applications at scale across hybrid cloud environments. Built on Kubernetes, it provides a unified application platform supporting the entire lifecycle from building cloud-native apps to modernizing existing workloads. The platform delivers a consistent experience on self-managed infrastructure or managed cloud services on AWS, Azure, IBM Cloud, or OpenShift Dedicated.
As a comprehensive development and deployment platform, OpenShift serves enterprise IT, developers, and DevOps professionals accelerating innovation with security and reliability. It integrates with popular tools and frameworks for AI applications, virtualized workloads, and traditional apps. With built-in security and enterprise support, OpenShift is trusted for critical business applications. For more developer tools, check CI/CD Tool and IDE categories.
How to Use Red Hat OpenShift
To start with Red Hat OpenShift, choose a deployment model: self-managed editions or managed services on AWS, Azure, or IBM Cloud. Developers use the web console or CLI with built-in tools and operators for containerized apps. It supports automated CI/CD via OpenShift Pipelines and GitOps for consistent deployment across environments. Admins manage clusters, monitor with OpenShift Observability, and enforce security policies.
Core Features of Red Hat OpenShift
- Kubernetes-Based Container Platform - Enterprise-grade container orchestration with automated management and scaling capabilities
- Hybrid Cloud Deployment - Consistent application platform across on-premise, cloud, and edge environments
- Built-in Developer Tools - Comprehensive set of development tools, operators, and automation capabilities
- Advanced Security Features - Enterprise-grade security with built-in compliance and governance controls
- AI and Machine Learning Support - Complete platform for building, training, and deploying AI models at scale
Use Cases for Red Hat OpenShift
- Enterprise application modernization and cloud migration projects
- Building and deploying cloud-native microservices applications
- AI and machine learning model development and deployment
- Virtual machine migration and management through OpenShift Virtualization
- Multi-cluster management across hybrid cloud environments
- CI/CD pipeline automation for DevOps teams
- Enterprise container security and compliance management
Support and Contact
For technical support, visit the Red Hat OpenShift support portal or email contact@redhat.com. Enterprise customers get dedicated support via subscription. Documentation, training, and forums are on the customer portal.
Company Info
Red Hat, Inc., founded in 1993 and based in Raleigh, NC, is an IBM-owned company providing open source enterprise software, including operating systems, cloud computing, and container platforms.
Login and Signup
Access OpenShift via the official portal. Log in to manage clusters or contact sales for trials and purchasing.
Red Hat OpenShift FAQ
What is Red Hat OpenShift and how does it differ from standard Kubernetes?
Red Hat OpenShift is an enterprise Kubernetes platform that adds developer tools, security features, and management capabilities on top of standard Kubernetes for production workloads.
Can Red Hat OpenShift run traditional virtual machines alongside containers?
Yes, OpenShift Virtualization allows you to run and manage VMs alongside containerized applications on the same platform.
What deployment options are available for Red Hat OpenShift?
OpenShift offers self-managed editions and managed cloud services on AWS, Azure, IBM Cloud, and Red Hat OpenShift Dedicated.
How does OpenShift support AI and machine learning workloads?
OpenShift provides a complete platform for building, training, and deploying AI models at scale, with tools for data processing and model serving.
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