The growth of cloud-native applications is now one of the main drivers of digital transformation across industries. This approach enables organizations to build more flexible, scalable, and responsive applications that can adapt to changing market needs. It’s no surprise that many companies are adopting cloud-native principles as the foundation for their digital service development.
However, behind this great potential lies significant operational complexity. Infrastructure management, orchestration across environments, and efficient resource allocation often become major challenges. IT teams and developers must navigate rapidly evolving technical variables—slowing down the very innovation that cloud-native promises to accelerate.
Red Hat OpenShift, supported by Red Hat OpenShift AI, offers an integrated solution to speed up AI adoption and simplify the management of cloud-native applications across hybrid environments. This article explores how the platform addresses key technical challenges through an AI-driven cloud optimization approach while supporting the development of scalable, intelligent applications.
Why Intelligent Applications and Generative AI Are Becoming Essential
The rise of cloud-native applications is pushing organizations to make AI part of their modernization strategies. In practice, however, operationalizing AI/ML can take months—while innovations in generative AI are evolving by the day. This gap can be risky, especially when teams struggle to keep up with tools, manage resources like GPUs, or work within a unified platform.
To close this gap, organizations need a system that unites data science, engineering, and IT teams into one efficient and flexible ecosystem—enabling rapid development, training, and deployment of AI models, regardless of where the workloads run.
Getting to Know Red Hat OpenShift AI
Red Hat OpenShift AI is an open-source platform built to accelerate the AI model lifecycle—from experimentation to training and deployment—within hybrid cloud environments. It enables data scientists, engineers, and developers to work collaboratively within a consistent and scalable ecosystem. With built-in support for popular tools like Jupyter, TensorFlow, and PyTorch, and MLOps integration through Kubeflow, team collaboration becomes more seamless.
OpenShift AI’s strength lies in its ability to bring AI-enabled applications to production faster. Features like self-service access, automated resource scaling, and GPU-based workload management help reduce operational complexity without sacrificing performance. The platform is also highly flexible—offered as either a self-managed or fully managed service across various cloud providers, allowing businesses to choose a deployment model that fits their needs.
The Evolution of OpenShift: From Application Orchestration to an AI-Ready Platform
OpenShift has evolved alongside the changing needs of the industry—from its roots as a container orchestration solution to a comprehensive platform for end-to-end AI development. The following table outlines how its capabilities have expanded to address increasingly complex technology demands.
| Feature Category | Red Hat OpenShift (Non-AI) | Red Hat OpenShift AI |
| Core Functionality | Kubernetes-based container orchestration platform | AI/ML-enhanced platform for managing AI workloads |
| AI/ML Support
| Limited native AI/ML support | Integrated AI/ML tools for model training, deployment, and serving |
| Model Management | No built-in model registry | Centralized model registry for versioning and tracking |
| Data Management | General data handling for applications | Data drift detection to monitor input data changes |
| Bias Detection | No AI fairness tools | Tools to detect and mitigate model bias |
| Fine-Tuning Model | Traditional application scaling | LoRA-based efficient fine-tuning for large language models |
| GPU Acceleration | Supports NVIDIA and AMD GPUs for containerized workloads | Optimized AI/ML workload execution with NVIDIA NIM and AMD ROCm |
| Hybrid Cloud Support | Deploys workloads across hybrid cloud environments | AI model training and inference across hybrid and multi-cloud environments |
| MLOps Integration | Requires third-party tools | Built-in MLOps foundation with Red Hat Consulting support |
| Open Source Projects | Based on Kubernetes and OpenShift ecosystem | Integrates Kubeflow and TrustyAI for AI model lifecycle |
Read More: What Are the Advantages of RHEL AI as a Generative AI Solution for Business?
6 Major Challenges of Cloud-Native and Hybrid Apps—and How OpenShift AI Solves Them
Running cloud-native applications in hybrid environments presents a variety of challenges—from complex deployments and rising costs to rapidly outdated AI models. OpenShift AI delivers integrated, flexible solutions ready for large-scale use. Here are six key challenges and how OpenShift AI helps solve them.
1. Complex Deployments and Infrastructure Scaling
Each cloud-native application may run in environments with different dependencies and configurations, causing inconsistent and time-consuming deployments. OpenShift standardizes container orchestration with Kubernetes, while OpenShift AI enhances it with a Model Registry—enabling centralized storage, versioning, and tracking of AI models for better lifecycle management and auditability.
2. Limited Agility in Scaling and Resource Efficiency
Slow horizontal scaling and poor resource allocation often create bottlenecks during traffic spikes. OpenShift supports dynamic autoscaling and multi-cloud orchestration. OpenShift AI complements this with native GPU acceleration through NVIDIA NIM and AMD ROCm—optimizing AI inference and reducing time-to-value for intelligent applications.
3. Model Drift from Unstable Real-Time Data
Constantly changing data can cause machine learning models to drift and lose accuracy. OpenShift AI provides Data Drift Detection to automatically monitor input data against the original training set. With customizable pipelines, data science teams can implement automated retraining based on dynamic parameters.
4. Lack of Transparency and Fairness Controls in AI Models
In critical decision-making scenarios, ensuring fairness and explainability is essential. Undetected bias can lead to discriminatory outcomes. OpenShift AI includes Bias Detection Tools from the TrustyAI ecosystem, which can be integrated into model pipelines for fairness evaluation during training and inference.
5. High Operational Overhead for Model Training
Training and fine-tuning large language models (LLMs) can be resource-intensive, requiring significant GPU and compute power. OpenShift AI supports efficient fine-tuning with LoRA (Low-Rank Adapters), allowing model updates with a minimal resource footprint—reducing memory and compute consumption without sacrificing accuracy.
6. Fragmented MLOps Tools and Disconnected Pipelines
Many AI/ML teams still manage model lifecycles manually using disconnected tools, leading to gaps in CI/CD processes. OpenShift AI integrates fully with Kubeflow and Open Data Hub, providing workflow builders, experiment tracking, and automated pipeline execution in one end-to-end platform.
How Red Hat Saved Over $5 Million with OpenShift AI
With over 30,000 new support cases every month, Red Hat faced growing pressure to maintain operational efficiency and fast response times. To solve this, they built a suite of AI-powered solutions using OpenShift AI and Red Hat Enterprise Linux AI. These included intelligent article recommendations for troubleshooting and automatic summarization tools for case handoffs—all deployed in a GPU-accelerated hybrid cloud environment.
The results? More than $5 million in operational savings, a significant boost in user satisfaction, and greater support team efficiency. Tasks that were previously repetitive are now automated, dramatically reducing customer response times. This initiative is living proof that the right AI platform can deliver real, practical, and measurable impact.
Explore the Potential of OpenShift AI for Your Cloud-Native Stack with Virtus
Virtus Technology Indonesia (VTI) delivers Red Hat OpenShift AI solutions to help organizations accelerate AI operations and simplify the management of cloud-native applications—especially in achieving consistent model deployment, efficient GPU usage, and seamless MLOps integration.
As a part of Computrade Technology International (CTI) Group, Virtus is ready to support you from initial consultation to implementation and ongoing support—backed by a team of experts with deep experience in deploying AI-ready solutions on OpenShift.
Get in touch today and bring your cloud-native AI performance to the next level—with trusted solutions from Virtus.
Author: Danurdhara Suluh Prasasta
CTI Group Content Writer
