Jul 4, 2025
Deploying AI models can be challenging, but OpenShift makes the process faster and easier compared to Kubernetes. While Kubernetes is a powerful container orchestration tool, OpenShift builds on it by offering pre-configured tools, guided installation, and enterprise-grade support.
| Feature | Kubernetes | OpenShift |
|---|---|---|
| Setup Time | 2–3 weeks (manual configuration) | 2–3 days (guided installation) |
| Built-in Tools | Requires third-party integrations | Includes CI/CD, registry, and UI |
| Support | Community-driven | 24/7 enterprise-grade support |
| Security | Manual configurations | Pre-configured policies |
| AI Features | Requires custom setup | Integrated AI-specific tools |
OpenShift is a better choice for teams seeking faster deployment, reduced complexity, and integrated features tailored for AI. Kubernetes is suitable for those with advanced DevOps expertise and a preference for custom solutions.
The installation process lays the groundwork for your entire AI deployment experience. Here, we’ll explore the contrasts between Kubernetes' detailed manual setup and OpenShift's more streamlined, guided approach.
Setting up Kubernetes from the ground up demands a high level of technical expertise and manual effort. You'll need to handle networking configurations - like setting up Calico or Flannel - and establish storage classes for persistent volumes. This piecemeal approach not only increases complexity but also raises the chances of errors during installation.
Using kubectl effectively requires a deep understanding of YAML files and Kubernetes architecture. For AI workloads, configuring storage for large datasets and model artifacts involves manually creating storage classes and persistent volume claims. Even for seasoned engineers, this process can take several days to execute properly.
OpenShift simplifies installation with its automated, guided workflows. It offers two main installation methods: Installer-Provisioned Infrastructure (IPI), which automates the entire setup, and User-Provisioned Infrastructure (UPI), which provides structured guidance for custom environments.
The installation wizard walks you through each step, automatically handling networking, storage, and security configurations. OpenShift’s oc command-line tool builds on kubectl, adding extra functionality to ease the learning curve for Kubernetes-savvy teams. Additionally, OpenShift includes a web console from the start, enabling teams to manage deployments and troubleshoot without relying heavily on command-line tools. For AI teams working with platforms like NanoGPT - which supports a variety of text and image generation models - this visual interface can significantly speed up the transition from experimentation to production.
The differences in installation complexity directly impact how quickly teams can deploy AI solutions. In the fast-paced world of AI, deployment speed is essential for staying competitive. Companies with strong AI strategies often outperform their peers, making it critical to minimize delays. For instance, slow implementation timelines can lead to missed opportunities and financial setbacks.
OpenShift’s user-friendly installation process addresses these challenges by reducing setup times. While a typical Kubernetes deployment might take weeks of configuration and testing, OpenShift’s guided approach can cut that down to just a few days. This efficiency allows teams to shift their focus to fine-tuning AI performance, improving ROI, and staying agile in a competitive market.
| Setup Aspect | Kubernetes | OpenShift |
|---|---|---|
| Installation Time | Approximately 2–3 weeks | Approximately 2–3 days |
| Networking Setup | Manual configuration needed | Automated during installation |
| Storage Integration | Manual setup required | Pre-configured storage |
| Monitoring Tools | External tools required | Built-in monitoring |
| Learning Curve | Steep (YAML and kubectl expertise) |
Moderate (includes web console) |
The tools and workflows offered by a platform can significantly shape a developer's experience and, ultimately, the success of AI projects. While both OpenShift and Kubernetes are capable of managing AI workloads, they provide developers with very different approaches to handling machine learning tasks. Let’s dive into how each platform supports developers throughout the AI workflow.
OpenShift comes equipped with an array of built-in tools designed to simplify AI model development. These include OpenShift Pipelines (powered by Tekton), OpenShift Build for creating containers, OpenShift GitOps for continuous deployment, and Red Hat Quay, which serves as an integrated image registry.
OpenShift AI also offers a sandbox environment where teams can develop, train, and test machine learning models in the public cloud before transitioning them to production. For teams requiring high-performance computing, the platform integrates seamlessly with hardware accelerators like NVIDIA GPUs through the Red Hat certified GPU operator, ensuring easier access to the resources needed for intensive model training.
"Red Hat® OpenShift® helps organizations improve developer productivity, automate CI/CD pipelines, and shift their security efforts earlier and throughout the development cycle."
- Red Hat
A real-world example of OpenShift's impact is Banco Galicia, which developed an AI-based natural language processing solution on the platform. This innovation cut verification times from days to minutes, achieving 90% accuracy, while also reducing application downtime by 40%. Such results highlight how OpenShift’s built-in tools can accelerate AI project timelines and improve efficiency.
Unlike OpenShift, Kubernetes does not come with native CI/CD functionalities. Instead, it relies on third-party tools like Jenkins, GitLab CI, or Tekton to manage continuous integration and deployment. While this approach allows teams to choose tools tailored to their specific needs, it also brings added complexity in terms of setup, configuration, and ongoing maintenance.
For AI workflows, this fragmented ecosystem means developers must piece together separate tools for tasks like image registry management, deployment pipelines, and workflow automation. This extra layer of complexity can slow down development cycles and increase the likelihood of errors or failures in the AI deployment pipeline.
Once installation is complete, maintaining developer productivity becomes a critical factor. OpenShift’s integrated environment provides a distinct advantage here, especially for AI development teams. Its DevOps automation capabilities extend into the machine learning lifecycle, fostering collaboration among data scientists, developers, and IT operations. This collaboration is essential when model development and deployment need to be tightly aligned.
"Extending OpenShift DevOps automation capabilities to the ML lifecycle enables collaboration between data scientists, software developers, and IT operations so that ML models can be quickly integrated into the development of intelligent applications. This helps boost productivity, and simplify lifecycle management for ML powered intelligent applications."
- Red Hat
With its streamlined workflow, OpenShift supports continuous model integration and rapid redeployment - key factors for teams working with diverse AI models, such as those leveraging NanoGPT, to keep up with competitive deployment speeds.
When it comes to managing AI model deployments, OpenShift and Kubernetes take different approaches to automation. While both platforms are capable of handling AI workloads, the way they manage deployments can significantly impact the speed and reliability of updating models in production. Automation is a key factor here, as it helps reduce manual steps, ensuring smoother and more efficient processes. OpenShift, in particular, takes strides to simplify these workflows, making it easier to keep production environments running reliably.
Kubernetes uses Deployment objects to manage application rollouts. These objects rely on a controller loop that prioritizes availability during updates. OpenShift, on the other hand, has traditionally relied on DeploymentConfigs, which focus on maintaining consistency. DeploymentConfigs use a deployer pod for each rollout and include features like automated triggers and lifecycle hooks. These tools can automatically initiate deployments when base images or configurations are updated.
However, OpenShift is evolving. Starting with OpenShift Container Platform 4.14, DeploymentConfigs are being deprecated in favor of standard Kubernetes Deployments. To fill the gap, OpenShift now leans on tools like OpenShift GitOps and Helm, which offer similar capabilities while aligning with Kubernetes' native deployment practices.
OpenShift simplifies AI deployments by offering built-in tools and automation that reduce complexity. In November 2024, Red Hat introduced OpenShift AI 2.15, which brought several new features designed to streamline AI workflows. These include:
Joe Fernandes, Vice President and General Manager of Red Hat's AI business unit, highlighted the platform's advancements:
"The latest version of Red Hat OpenShift AI offers significant improvements in scalability, performance and operational efficiency while acting as a cornerstone for the overarching model lifecycle, making it possible for IT organizations to gain the benefits of a powerful AI platform while maintaining the ability to build, deploy and run on whatever environment their unique business needs dictate."
These features reduce the need for manual integration of third-party CI/CD, model serving, and monitoring tools. Instead, OpenShift provides a more unified and automated approach, making continuous delivery processes less cumbersome.
When it comes to continuous delivery, OpenShift provides a streamlined experience with its integrated DevOps tools. OpenShift Pipelines, a Kubernetes-native CI/CD framework, and OpenShift GitOps, which uses Argo CD as its engine, are key components of this ecosystem. These tools are especially valuable considering Gartner's estimate that only 54% of AI projects move beyond the pilot stage and into production.
When deploying AI models in production, having dependable support and strong security measures is critical. These elements ensure operational stability and safeguard data, complementing the streamlined deployment and automation capabilities previously discussed.
Red Hat offers round-the-clock professional support for OpenShift, ensuring that critical AI deployment issues are addressed promptly. In continuous AI operations, reducing downtime is a top priority.
OpenShift's support system is tailored to meet the varied needs of enterprises. For instance, its Enhanced Solution Support tier provides a high level of coverage, including a 15-minute initial response time, a 4-hour restoration target, and a 20-day resolution commitment.
| Support Tier | Initial Response | Restoration Time | Resolution Time |
|---|---|---|---|
| Standard | 1 business hour | - | - |
| Premium | 1 hour | - | - |
| Enhanced Solution Support | 15 minutes | 4 hours | 20 days |
This structured approach ensures quick resolutions, helping maintain the availability of AI models. Beyond support, OpenShift also delivers integrated security and compliance features, which are essential for enterprise AI deployments.
OpenShift incorporates stringent default security policies and Security Context Constraints (SCCs) to protect sensitive AI data. These measures automatically enforce security boundaries, reducing the risk of unauthorized access to models and training data.
For AI-specific needs, OpenShift AI includes guardrails to secure model inputs and outputs, protecting them from harmful or unintended data interactions. Additionally, the platform provides a curated library of production-ready, validated models optimized for OpenShift AI. This helps teams maintain control over model access while adhering to strict security policies.
OpenShift also simplifies security management by integrating a built-in monitoring stack and compliance operator. This reduces reliance on multiple third-party security tools, thereby minimizing potential vulnerabilities.
Kubernetes benefits from a large community, with over 88,000 contributors from more than 8,000 companies. However, its community-driven support model can be challenging for mission-critical AI applications. Assistance from forums or documentation may not be immediate, especially during off-peak hours.
Kubernetes support often relies on community resources and vendor-specific arrangements, leading to inconsistent response times that may not align with the demands of AI deployments. Additionally, Kubernetes' flexibility means many security configurations must be handled manually, requiring specialized expertise. Organizations frequently need to build internal DevOps teams or depend on third-party vendors for enterprise-grade support.
Given that 96% of enterprises use Kubernetes, these challenges are widespread. Many businesses eventually migrate to platforms like OpenShift to gain access to unified support and integrated security features specifically designed for complex AI environments.
OpenShift and Kubernetes cater to different needs when it comes to deploying AI workloads. While both are capable platforms, their approaches and features differ significantly, especially in how they address organizational priorities and technical demands.
To summarize, OpenShift provides an enterprise-ready, all-in-one solution that reduces deployment time, allowing AI teams to focus more on model development. Its guided installation process and automation features simplify setup by eliminating many of the manual steps typically associated with Kubernetes.
For instance, companies like Amadeus have reported faster deployment times and smoother development workflows with OpenShift. This is largely due to OpenShift’s streamlined design, which avoids the manual complexity often encountered with Kubernetes.
When it comes to AI-specific needs, OpenShift goes a step further by including features like bias detection and AI guardrails. These tools are particularly helpful for organizations operating in regulated industries, where compliance is a top priority.
Another standout feature of OpenShift is its enterprise support model. While Kubernetes is backed by a vast community of 5.6 million developers, mission-critical AI applications often require the 24/7 support and advanced security measures that OpenShift provides. The following table outlines the key differences between the two platforms:
| Feature | Kubernetes | OpenShift |
|---|---|---|
| Installation and Setup | Requires manual configuration | Streamlined, guided setup |
| Integrated Tools | Relies on third-party integrations | Includes built-in registry, CI/CD, and web UI |
| Deployment Automation | Uses deployment objects and controllers | Features DeploymentConfig with triggers and hooks |
| Support and Security | Community-driven support | 24/7 enterprise support with advanced security |
| AI Model Management | Requires manual integration | Offers a unified platform with AI-focused features |
| Developer Experience | DIY approach | Pre-integrated, opinionated stack |
In essence, Kubernetes provides flexibility for seasoned DevOps teams, but OpenShift stands out by offering faster deployment and reduced operational complexity - qualities that are especially valuable for organizations aiming to accelerate their time-to-market.
For AI projects specifically, OpenShift’s integrated features address many of the challenges that can slow down machine learning initiatives. Its AI API server and direct access to essential components significantly reduce the integration hurdles often faced when building a custom AI infrastructure on Kubernetes.
While Kubernetes serves as a strong foundation, OpenShift emerges as the more comprehensive solution for scaling AI deployments.
OpenShift comes equipped with strong security and compliance tools, making it a great fit for industries with strict regulations. It includes hardware-based encryption for confidential clusters, ensuring data stays protected even during processing. Additionally, it offers advanced access controls and encryption to safeguard sensitive information. To streamline compliance, OpenShift features compliance operators that automate security checks and help organizations stay aligned with industry standards.
These capabilities allow businesses to meet tough regulatory demands while keeping data secure, making OpenShift a dependable option for fields like healthcare, finance, and government.
OpenShift's enterprise support equips organizations with the necessary tools and resources to manage mission-critical AI applications effectively. It prioritizes strong security measures, adherence to industry standards, and dependable support for handling complex AI workloads at scale.
Some standout advantages include smooth lifecycle management, improved performance with hardware acceleration, and the flexibility to manage workloads across various cloud platforms. This robust support helps reduce downtime, lowers operational risks, and ensures consistent high availability - key factors for organizations that depend on AI to drive essential operations.
OpenShift makes deploying AI models easier by providing a comprehensive platform that covers every step - from preparing data to training models, deploying them, and keeping them running smoothly. With built-in tools and automation, it takes much of the hassle out of managing AI workflows, allowing teams to spend more time on creating new solutions rather than dealing with infrastructure challenges.
By simplifying these processes and supporting hybrid cloud setups, OpenShift helps businesses roll out AI solutions faster. This not only boosts productivity but also gives companies an edge by enabling quicker decisions and more efficient operations.
