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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.
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.
With this comparison in mind, let’s take a closer look at each solution, starting with RHEL AI.
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:
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.
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:
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 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:
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:
For companies focusing more on high availability and resilient AI operations, OpenShift AI is the better platform for scalable, production-grade AI deployments.
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:
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.
Selecting between RHEL AI and OpenShift AI depends on your AI development strategy and scalability needs.
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.
