Some projects need minimal overhead and fast results. Others require large-scale orchestration and deep integration. For your project, the ideal AI setup will fit your immediate needs without hampering your future ambitions.
Red Hat addresses these challenges with two paths: Red Hat Enterprise Linux (RHEL) AI for simpler deployments and OpenShift AI for scaling complex environments. RHEL AI integrates with existing workflows and aims at smaller workloads, while OpenShift AI enables advanced pipelines and cluster-level coordination for bigger projects. Both solutions align with different stages of an AI journey.
This guide will unpack each’s strengths and help you decide which is best for your project and when to deploy it.
Comparing RHEL AI and OpenShift AI
For organizations evaluating Red Hat’s AI solutions, this list highlights the core differences between RHEL AI and OpenShift AI in terms of deployment, scalability and automation.
- Deployment model
- RHEL AI: Single production server
- OpenShift AI: Distributed Kubernetes across hybrid clouds
- Complexity and setup
- RHEL AI: Straightforward
- OpenShift AI: Advanced capabilities, more robust features
- Scalability
- RHEL AI: Good for smaller AI projects
- OpenShift AI: Designed for mid- to large-scale AI
- Machine learning operations (MLOps) automation
- RHEL AI: Integrated but simpler
- OpenShift AI: Comprehensive pipeline automation
- Open source tools
- RHEL AI: Granite LLMs, InstructLab, vLLM, Docling
- OpenShift AI: Granite LLMs, InstructLab, Open Data Hub, vLLM, KubeFlow, partner integrations
- Ideal use cases
- RHEL AI: On-premises, smaller scale
- OpenShift AI: Hybrid cloud, enterprise AI at scale
- Cloud and partner integration
- RHEL AI: Limited support from partners
- OpenShift AI: Extensive cloud and partner ecosystem integration
With this comparison in mind, let’s take a closer look at each solution, starting with RHEL AI.
RHEL AI: A Foundation for Individual Servers
RHEL AI is an easy-to-deploy, server-centric AI platform that efficiently runs on standalone servers (on premises or in the cloud) for organizations seeking a straightforward generative AI (GenAI) solution. It removes the burden of large-scale orchestration overhead, which is ideal for teams that want to focus on developing AI without managing distributed infrastructure. It is also best suited for teams focused on AI development while maintaining data privacy and security.
Some of its key benefits include:
- IBM Granite LLMs, which allow for rapid prototyping with pre-trained AI models.
- InstructLab, which simplifies model alignment and fine-tuning with minimal setup.
- Ability to run pre-trained AI models locally without requiring dynamic scaling.
- A simpler architecture that reduces maintenance and lowers consumption of IT resources.
Smaller teams, research institutions and businesses with strict data governance policies can benefit from RHEL AI. For many organizations, especially those in the early stages of AI adoption, this lightweight yet capable platform is more than enough to get started.
Why Starting With RHEL AI Makes Sense
The best approach is often to start small, and RHEL AI allows this with its easy setup, lower expense and incremental adoption of AI. It’s good for teams exploring AI without committing to complex platforms. Although powerful, Kubernetes orchestration can add unnecessary complexity early on. This makes RHEL AI a practical choice before scaling up.
Aside from its ease of use, RHEL AI also offers flexibility. It accommodates open source AI frameworks, allowing you to test AI models without being held hostage to vendors. This makes it a good fit for research teams and startups that must prove AI use cases prior to scaling.
However, while RHEL AI is effective for smaller projects, it lacks features for large-scale AI operations. Some of its limitations are:
- No distributed, multicluster AI training — it’s not suitable for organizations handling complex, high-volume workloads.
- Limited automation — it lacks the advanced MLOps tools available in OpenShift AI.
Organizations with long-term AI ambitions may start with RHEL AI but should plan for a transition to a more scalable solution as workloads expand.
OpenShift AI: Built for Scalable, Enterprise-Grade AI
OpenShift AI provides a platform for building, training, deploying and monitoring predictive and generative AI models. It offers orchestration, automation and scalability for large-scale AI workloads on multiple hybrid cloud environments. It also includes Kubernetes-native scalability, making it capable of effectively scheduling and carrying out resource allocation for demanding AI applications.
OpenShift AI offers a number of advantages, including:
- Dynamically scaling AI workloads across distributed infrastructure.
- Automating AI model training, deployment and monitoring through data science pipelines, making it easier to run operations.
- Supporting a unified platform for AI model management from on-premises to the cloud with minimal manual overhead.
- Conforming to security and compliance practices including role-based access control (RBAC), trustworthy AI for bias detection and guardrails to protect organizations from harm.
- Adding custom cluster images to enhance collaboration on notebooks and model registry to track and share data science projects.
Organizations with multiple models or medium to large AI workloads need a platform that offers scalability, security and compliance. OpenShift AI is good for businesses looking to build ML pipelines and those with firm regulatory requirements, such as:
- Large public-sector organizations that run multiple AI applications on hybrid cloud platforms.
- Financial institutions where AI security, compliance and risk management are crucial.
- Health and biotech firms that rely on AI for drug development and medical diagnostics.
For companies focusing more on high availability and resilient AI operations, OpenShift AI is the better platform for scalable, production-grade AI deployments.
Not Every AI Project Needs This Much Overhead
While OpenShift AI offers quite a number of benefits, including scalability and orchestration, it requires a steep learning curve and infrastructure requirements that not every organization is prepared to undertake. Here are some tradeoffs associated with OpenShift AI:
- Managing Kubernetes-based AI workloads requires skilled expertise.
- Higher operational complexity means that advanced features require more setup, maintenance and monitoring.
- Robust automation and multicloud features typically demand greater investments in infrastructure and resources.
The overhead may outweigh the benefits for smaller teams or those just starting with AI. However, for companies concentrating on scalability, automation and resilience, OpenShift AI remains a strategic long-term option.
For example, a retail company managing AI-driven recommendations across multicloud infrastructure would benefit from OpenShift AI’s model monitoring and performance optimization to achieve a cost-effective solution for AI workloads at scale. Meanwhile, a research institution with strict data privacy requirements may choose RHEL AI for its lightweight, on-premises deployment, avoiding cloud complexity.
Which AI Solution Is Right for You?
Selecting between RHEL AI and OpenShift AI depends on your AI development strategy and scalability needs.
- RHEL AI is ideal for server-centric AI workloads, individual deployments and simpler AI use cases.
- OpenShift AI thrives in multicloud environments, offering enterprise-grade AI orchestration, MLOps automation and large-scale AI model training and inferencing.
For Red Hat shops, a balanced strategy involves starting with RHEL AI for experimental or small-scale AI models. Organizations can then transition to OpenShift AI when AI workloads demand hybrid cloud infrastructure, scalable AI and enterprise support.
Making the right AI platform choice improves adoption and scalability as your needs evolve. The key to success is planning ahead for AI expansion.
The post Choosing the Right Red Hat AI Solution: RHEL AI vs. OpenShift AI appeared first on The New Stack.