Red Hat OpenShift the most mature and stable Kubernetes solution on the planet
July 05, 2025
Red Hat OpenShift the most mature and stable Kubernetes solution on the planet
Score 10 out of 10
Vetted Review
Verified User
Software Version
Red Hat OpenShift Container Platform (self-managed)
Modules Used
- OpenShift Data Science
Overall Satisfaction with Red Hat OpenShift
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.
- 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.
In our experience, we do not rely on the full application development lifecycle tools provided by Red Hat OpenShift, such as Pipelines, GitOps, or the Developer Hub. Our focus is primarily on research and experimentation rather than traditional application delivery. However, components like Red Hat OpenShift AI and Red Hat OpenShift Virtualization have proven valuable by enabling flexible resource allocation and advanced AI workloads. While we don't leverage the full lifecycle stack, Red Hat OpenShift still supports our efficiency by offering a stable, scalable, and secure environment for running Jupyter-based quantum simulations and AI inference services, which are central to our research-driven goals.
We primarily run Red Hat OpenShift on-premises to support our research infrastructure, including AI inference services, quantum simulation with IBM Qiskit, and Jupyter-based development via Red Hat OpenShift AI. While we do not use Red Hat OpenShift for traditional application delivery or lifecycle management, its consistent infrastructure and container orchestration capabilities have enabled us to experiment across a wide range of research domains. The platform’s flexibility would allow us to extend to public cloud or edge environments if needed, ensuring reproducibility and portability of our workloads. This consistency is crucial for maintaining stable, secure, and scalable environments in high-performance research settings.
- HPE Ezmeral Data Fabric (MapR) and HPE Ezmeral Machine Learning Ops
HPE Ezmeral has been a complete disaster in our experience, especially when compared to Red Hat OpenShift. The platform feels clunky and unfinished, with poor integration across components and a clear lack of maturity that results in frequent instability and unpredictable behavior. Rather than streamlining operations, Ezmeral introduces new issues and overhead, wasting valuable time and resources. In contrast, Red Hat OpenShift offers a stable, well-documented, and highly integrated environment that simply works. Its broad ecosystem, consistent performance, and enterprise-level support make it far more suitable for demanding research and AI workloads. Red Hat OpenShift is in a different league entirely—Ezmeral doesn’t come close.
Do you think Red Hat OpenShift delivers good value for the price?
Yes
Are you happy with Red Hat OpenShift's feature set?
Yes
Did Red Hat OpenShift live up to sales and marketing promises?
Yes
Did implementation of Red Hat OpenShift go as expected?
Yes
Would you buy Red Hat OpenShift again?
Yes
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