Red Hat OpenShift

Score9.3 out of 10

401 Reviews and Ratings

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

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

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

#1 most frequent

Professional, Scientific, and Technical Services

31.4%

3,327 installations of 10,606

“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.”
— Senior Principal, Korn Ferry (1001-5000 employees)

#2 most frequent

Information

14.8%

1,572 installations of 10,606

“Easily deploy Kubernetes Pods in a user friendly way”
— Systems Engineer, paychex (10,001+ employees)

#3 most frequent

Finance and Insurance

13.7%

1,448 installations of 10,606

“I deploy the core components for application development teams. Red Hat OpenShift allows us to quickly spin up cluster for our app teams.”
— Sr Systems Engineer, Visa (10,001+ employees)

Rainer Weninger

Platform Engineer in Information Technology at SCCH (51-200 employees employees)

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

Victor Williams

Linux Administrator in Information Technology at Blue Cross from Blue Shield of Alabama (1001-5000 employees employees)

Use Cases and Deployment Scope

Primarily right now we're doing with VMs. We were a Rev customer and migrated our Rev VM workloads to OpenShift virtualization. We also have our eye on the future with container environment, so it fits perfectly into what we were doing now and what we look to do in the future.

Pros

It's a one pane of glass, so when we have Rev only it was a hypervisor for VMs. OpenShift, you can put Ansible in it, you can hook into satellite, it can do with OpenShift AI. You can do AI models and stuff like that. So I think it's more like a Swiss Army knife rather than a fire extinguisher.

Cons

OpenShift virtualization has a little room for improvement. I'm coming from it as a Rev customer. There's some things in that OpenShift virtualization that were in Rev that I would like to see in OpenShift virtualization. I realized that they're chasing the VMware crowd and that's fine, but from us old Rev customers, we'd like to see some things that was in Rev around via migration and things of that nature that could be in OpenShift virtualization, I hope is being planned to be put in.

Return on Investment

It has allowed us to see where we need to be in the container world. I'm going to call it a net neutral impact, not negative or positive. It has given us a sense of what we are ready for and what we're not ready for. You know where you stand.
You don't know what you don't know, so it helps us know what we want to know.

Alternatives Considered

Kubernetes

Other Software Used

Red Hat Ansible Automation Platform, Red Hat Enterprise Linux (RHEL)

Usability

Steven Pazienza

Systems Engineer in Engineering at Collins Aerospace (10,001+ employees employees)

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)

Verified User

Professional in Information Technology (10,001+ employees employees)

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

Verified User

Engineer in Information Technology (1001-5000 employees employees)

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

Azure Kubernetes Service (AKS)

SUSE Rancher

Amazon Elastic Kubernetes Service (EKS)

Tanzu Application Platform

Red Hat OpenShift

What is Red Hat OpenShift?

Categories & Use Cases

Key Features

Top Performing Features

Scalability

Development environment creation

Ease of building user interfaces

Areas for Improvement

Platform management overhead

Issue monitoring and notification

Issue recovery

Most Frequent Users

Professional, Scientific, and Technical Services

Information

Finance and Insurance

Reviews

Use Cases and Deployment Scope

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Alternatives Considered

Other Software Used

Use Cases and Deployment Scope

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Return on Investment

Alternatives Considered

Other Software Used

Usability

Use Cases and Deployment Scope

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Other Software Used

Use Cases and Deployment Scope

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Alternatives Considered

Other Software Used

Usability

Use Cases and Deployment Scope

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Cons

Return on Investment

Alternatives Considered

Other Software Used

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