#1 most frequent
United States of America
36.7%3,896 installations of 10,628
What is Red Hat OpenShift?
OpenShift is Red Hat's Cloud Computing Platform as a Service (PaaS) offering. OpenShift is an application platform in the cloud where application developers and teams can build, test, deploy, and run their applications.
Categories & Use Cases
Videos
Top Performing Features
Scalability
Ease of scaling up or down to meet demand
Category average: 8.2
Development environment creation
Ease of creating new development environments
Category average: 8
Ease of building user interfaces
Ability to build flexible user interfaces using drag-and-drop tools
Category average: 7.9
Areas for Improvement
Platform management overhead
Resources required to keep platform up and running
Category average: 7.4
Issue monitoring and notification
Integrated monitoring and notification of issues and problems
Category average: 7.3
Issue recovery
Ease of recovery from problem state
Category average: 7.3
Who Buys & Uses Red Hat OpenShift
#1 most frequent
Professional, Scientific, and Technical Services
31.6%3,361 installations of 10,628
“it is a multi cluster environment where each stage, dev, QA and prod has their own Red Hat OpenShift app cluster managed by an management cluster. This i see is a good way to manage the multi cluster architecture. Also, provide prod like environment for the development community. Not sure about the edge, but we have a public cloud integration.”
#2 most frequent
Information
14.8%1,576 installations of 10,628
“Easily deploy Kubernetes Pods in a user friendly way”
#3 most frequent
Finance and Insurance
13.5%1,433 installations of 10,628
“I deploy the core components for application development teams. Red Hat OpenShift allows us to quickly spin up cluster for our app teams.”
#2 most frequent
India
5.9%631 installations of 10,628
#3 most frequent
Germany
5.0%531 installations of 10,628
Pros
- Streamlined application deployment and management processes
- Intuitive graphical user interface for easier operation
- Robust security features, including secure-by-default configurations
Cons
- Complexity in initial setup, configuration, and automation at scale
- Need for clearer and more comprehensive documentation and support
- Steep learning curve and user experience challenges for new users
Red Hat OpenShift the most mature and stable Kubernetes solution on the planet
Use Cases and Deployment Scope
We use Red Hat OpenShift as a flexible MLOps platform through OpenDataHub, enabling streamlined model training, tracking, and deployment workflows. It serves as the backbone for our AI Inference Server, allowing us to scale and manage containerized inference endpoints efficiently. Additionally, Red Hat OpenShift hosts our IBM Qiskit development environment via JupyterHub, supporting quantum computing research and prototyping. This setup addresses challenges in deploying reproducible ML pipelines, managing compute resources, and integrating emerging technologies like quantum computing. The scope includes AI/ML development, automated deployment, and hybrid cloud scalability across our research and enterprise infrastructure.
Pros
- Hosting Red Hat OpenShift AT (OpenDataHub)
- LORA Training for Models
- Hositng Inference Systems with MCP Connections
- Running Development Pods for Research Projects
Cons
- The complexity. Some errors occur of systems that cant interact with each other I even dont know run. The system is way to complex in its structure. It is not a OCP issue itself but Kubernetes. To get more adapted, it must be much more integrated and stable.
- The UI is part of the Red Hat OpenShift Container Platform. It should also be on the Red Hat OpenShift Kubernetes Engine (in a simpler way)
- Update Process is failing way too often. There are always issues.
- The User enforcement cant be used in our environment. We need root in pods per standard. This is quite complicated in Red Hat OpenShift.
Return on Investment
- As a research-focused organization, traditional Time-to-Market isn't a key metric for us—but Red Hat OpenShift has significantly expanded our ability to explore and prototype novel AI and quantum simulation workflows without infrastructure bottlenecks.
- The integrated OpenDataHub and JupyterHub environments have improved our productivity by providing a centralized, scalable platform for AI model development and quantum computing experiments.
- Red Hat OpenShift’s strong security model and operator lifecycle management have allowed us to safely experiment with cutting-edge technologies while maintaining a stable and compliant infrastructure.
- While operational costs are higher than simpler setups, the flexibility and innovation it enables have delivered strong research ROI.
Alternatives Considered
HPE Ezmeral Data Fabric (MapR) and HPE Ezmeral Machine Learning Ops
Other Software Used
Proxmox VE, VMware vSphere, Docker, Azure AI Studio
Red Hat OpenShift Review
Use Cases and Deployment Scope
As I assume for most companies using Red Hat OpenShift, we use the platform as an efficient and automating tool to manage and deploy our containerized applications across several environments. We often use Red Hat OpenShift for our Docker containers to package applications and their dependencies and monitoring their status to help us determine if any issues arise or develop from new code changes.
Pros
- Great at helping develop and manage containers
- Helps modernize our older applications
- Gives Enterprise-Grade features needed for developming our system for our customer.
Cons
- Reduce complexity for smaller teams/projects/companies
- More optimization of resource consumption.
- Improved debugging and troubleshooting tools.
Return on Investment
- Allow more time to work on other tasks
- decreased downtime
- improved productivity
Other Software Used
Elasticsearch, Kubernetes, Red Hat Enterprise Linux (RHEL)
Red Hat Openshift Platform.
Use Cases and Deployment Scope
We had a business requirement to integrate multiple systems, where we had developed a large number of APIs and microservices. To manage and deploy this large number of APIs and services, we used Red Hat OpenShift as a cloud platform to host and expose our endpoints to consumers who want to use the integration flow. OpenShift provides a significant edge in managing a large number of applications, offering features such as scaling, automated deployment, integration with monitoring tools, and resource quota usage statistics.
Pros
- We had a few microservices that dealt with notifications and alerts. We used OpenShift to deploy these microservices, which handle and deliver notifications using publish-subscribe models.
- We had to expose an API to consumers via MTLS, which was implemented using Server secret integration in OpenShift. We were then able to deploy the APIs on OpenShift with API security.
- We integrated Splunk with OpenShift to view the logs of our applications and gain real-time insights into usage, as well as provide high availability.
Cons
- It would be better to see the UI for Service Mesh enablement separately.
- It can enhance the console view, which displays application logs and the status of requests.
- Currently, we have a large number of APIs in a single namespace, which is difficult to view at a single point in time in OpenShift; perhaps we can see some improvement in the API lists and views.
Return on Investment
- Positive Impact: It significantly reduces the time and effort required to deploy and manage applications.
- Positive Impact: Offers excellent features and seamless integration support for other tools, helping businesses gain valuable insights and improve their performance.
- Negative Impact: The UI is somewhat clumsy and largish.
Alternatives Considered
Delinea Secret Server, Red Hat Fuse (discontinued) and Kali Linux
Other Software Used
AWS Cloud9, Splunk Enterprise, IBM API Connect
Usability
A developers great journey into the world of Devops
Use Cases and Deployment Scope
So, in my organization, we use Red Hat OpenShift as a devops container to host our applications. So every time we push a new version into our git azure, the pipeline is automatically triggered to build the new image and deploy our application. The big advantage we got from using Red Hat OpenShift is that when we used traditional virtual machines to deploy our applications, we didn't know when the service stopped and every time the VM stopped, we had to restart the application (which is essentially a .jar file) again, it didn't restart automatically. In addition, the application artifact is created manually using maven commands. Fortunately, all this was solved using Red Hat OpenShift, because in Red Hat OpenShift we just push the code to git and the magic happens automatically.
Pros
- build images
- deploy applications
- secure applications
- organize the pipeline between the source code and the deployed app
- great UI to explore the status of the app: dashboarding
Cons
- adding custom tasks to a predefined pipelines
- simplify the access for the logs
- simplify the add of custom resources (UI instead of yaml)
- documentation in the yaml files
Return on Investment
- increase security: enforce security system
- time saver: deployment is easier and faster
- increase target satisfaction: auto-recovery system, it restarts automatically when there is a problem
Alternatives Considered
Azure Kubernetes Service (AKS) and Amazon Elastic Kubernetes Service (EKS)
Other Software Used
Azure Kubernetes Service (AKS), Amazon Elastic Kubernetes Service (EKS), Google Kubernetes Engine
A paradigm shift in managing containers and VMs
Use Cases and Deployment Scope
Multi cluster setup. Identity management and security issues.
Pros
- Operators
- Ease of management
- Ability to use containers and VM's
Cons
- Backups and restore
- Storage provisioning
- SSO
Return on Investment
- ROI especially with the integration of virtualization to the same environment.
- Decreased downtime even during patching cycles
Alternatives Considered
SUSE Rancher
Other Software Used
Red Hat Ansible Automation Platform, HashiCorp Terraform, Tanzu Application Platform
