AI at Scale: Red Hat OpenShift AI as the Enterprise Backbone for Java Workloads

Experimenting with AI is easy. Running it in production is not.

That reality is becoming clearer to enterprise architects every day. Building a prototype with Quarkus and LangChain4j is straightforward: connect to a model, add retrieval, wrap it in a REST API, and you have an AI-powered application. But deploying that same application at enterprise scale raises entirely different questions. How do you manage GPU resources efficiently? How do you keep data pipelines compliant with regulations? How do you track which model version was used to make a critical business decision?

This is where platform thinking becomes critical. AI is not just another API call. It is a workload that has to be deployed, monitored, scaled, and governed with the same rigor as any other enterprise service. For Java developers, Quarkus provides the programming model and integration layer. But the operational backbone must come from a platform designed for AI at scale. That platform is Red Hat OpenShift AI.

From code to platform: Why Java alone is not enough

For decades, enterprise Java applications have been deployed on top of a managed platform. Whether it was WebSphere, JBoss EAP, or Tomcat in a Kubernetes cluster, the platform was always part of the story. Developers could focus on business logic because the platform handled clustering, resource management, and security.

AI workloads demand the same pattern, but the complexity is even higher. In addition to compute and memory, enterprises now need to schedule GPUs, manage distributed training and inference, control access to sensitive data, and meet compliance standards for explainability and bias detection. No application framework, not even Quarkus, can solve these challenges on its own.

What Quarkus can do is give developers a consistent way to write AI-infused applications. LangChain4j provides connectors to Granite models, watsonx, and vector databases. Jakarta EE and MicroProfile provide the long-term stability and interoperability developers expect. But without a platform to operationalize those applications, the enterprise story is incomplete.

Red Hat OpenShift AI: The operational backbone

OpenShift AI extends Red Hat’s Kubernetes-based OpenShift platform with the specific capabilities enterprises need to build, deploy, and manage AI workloads. It is not a separate product bolted onto Kubernetes; it is an integrated layer that brings together data science, machine learning, and application deployment.

For Java developers, this matters because you can treat AI workloads with the same discipline as any other enterprise service. When you deploy a Quarkus application that uses LangChain4j to call a Granite model, OpenShift AI ensures that the model runs on the right hardware, that data governance policies are enforced, and that observability is in place from end to end.

This platform-centric approach turns AI from an experimental add-on into an enterprise-ready capability. It is the difference between running a one-off proof of concept and scaling AI across multiple lines of business.

Connecting Quarkus to OpenShift AI

The value of OpenShift AI becomes clearer when you look at how it interacts with Quarkus. Developers can build AI applications using LangChain4j in Quarkus, connecting to watsonx.ai endpoints for model inference. With OpenShift AI, those endpoints can be backed by Granite models running inside the enterprise environment, with full lifecycle management in place.

That means developers do not need to change their code to take advantage of enterprise capabilities. The same Quarkus application that runs locally on a laptop can scale seamlessly on OpenShift AI, now benefiting from GPU scheduling, data lineage tracking, and enterprise-grade observability.

The result is a clean separation of concerns. Developers stay focused on the application logic. Platform teams use OpenShift AI to ensure that workloads run securely, efficiently, and in compliance with enterprise policies. Both sides operate within a consistent framework.

Governance and compliance at scale

One of the most pressing challenges for enterprise AI is governance. Regulators are increasingly requiring organizations to track how AI models are used, ensure transparency in decision-making, and protect sensitive data. Enterprises cannot afford to treat these requirements as afterthoughts.

OpenShift AI embeds governance into the platform. Model registries track versions and metadata. Pipelines record when models are trained, what data was used, and when they were deployed. Inference endpoints are monitored, and access can be controlled based on enterprise security policies. For industries like finance, healthcare, or government, these capabilities are not optional. They are the price of operating AI in production.

This governance model complements the application layer. A Quarkus application that calls a Granite model through LangChain4j can be fully auditable because OpenShift AI ensures that every request, response, and model version is tracked. This integration between framework and platform is what makes AI viable at enterprise scale.

Aligning with standards

Standards are not just about APIs. They are about providing a stable foundation for both technology and people. Jakarta EE and MicroProfile codify proven programming models so developers can apply their skills consistently across frameworks and runtimes. OpenShift AI plays a similar role at the platform level.

By aligning with open standards in Kubernetes, model serving, and data management, OpenShift AI avoids the lock-in risks of proprietary AI services. Enterprises can move workloads between clouds or run them on-premise without rewriting application code. Quarkus ensures portability at the application layer, while OpenShift AI ensures portability at the operational layer.

This alignment creates a full-stack story. Architects can design AI systems and know that their frameworks, models, and platforms will all remain interoperable and supported over time. It is the same lesson that made Java successful, applied to AI in the enterprise.

IBM’s role in securing the future

IBM and Red Hat are not just building products in isolation. They are investing across the spectrum to ensure that enterprise Java and AI remain aligned with open standards. On the framework side, Quarkus and LangChain4j provide the innovation layer. On the standards side, Jakarta EE and MicroProfile ensure long-term stability. On the AI side, Granite models offer open, transparent alternatives, while watsonx provides the governed, enterprise-grade environment. OpenShift AI ties all of this together as the operational backbone.

This is not just a product story, but a strategy to secure the future of enterprise applications. By investing in both fast-moving innovation and long-term stability, IBM is ensuring that enterprises can adopt AI today without putting their future at risk. Architects do not have to choose between speed and safety. They can have both.

Practical example: Scaling a customer support AI

Consider a retailer that is building an AI-powered customer support system. The first prototype might be a Quarkus application using LangChain4j to query a Granite model for answers. Locally, this is easy to run. But when deployed at enterprise scale, the system needs to handle thousands of concurrent requests, ensure that customer data is secure, and provide auditable logs of every model interaction.

With OpenShift AI, the retailer can deploy Granite models in a governed environment, enforce access controls, and use GPU resources efficiently. Quarkus applications can continue to evolve quickly, adding new features and refining prompts, while the platform ensures operational consistency. Over time, as RAG patterns and MCP protocols mature into Jakarta EE or MicroProfile standards, the application can migrate to those APIs without disruption.

The retailer does not just get an AI chatbot. They get a system that is scalable, secure, and aligned with enterprise governance requirements.

Turning AI experiments into trusted systems

For decision makers, the question is not whether AI can be prototyped in Java. That has already been proven. The real question is whether AI can be deployed at scale without introducing new risks. Without a platform like OpenShift AI, every team would build its own pipelines, governance layers, and monitoring systems. The result would be fragmentation, inconsistency, and high operational cost.

With OpenShift AI, enterprises get a consistent backbone for all AI workloads. Java developers can continue working with Quarkus and LangChain4j, data scientists can experiment with Granite and watsonx, and operations teams can manage deployments across the enterprise. Everyone works within the same framework, reducing complexity and increasing trust.

The platform that makes AI real for Java

AI is not just another API integration. It is a new class of workload that requires enterprise-grade operational support. Quarkus provides the speed and flexibility developers need to build AI-powered applications today. LangChain4j provides the integration layer to models and vector stores. Granite models and watsonx provide the foundation for open, governed AI. But it is OpenShift AI that turns these building blocks into a scalable, secure, and compliant enterprise platform.

For architects and IT decision makers, the lesson is clear. Experimenting with AI in Quarkus is valuable, but without a platform strategy, those experiments will never scale. OpenShift AI is the backbone that ensures innovation can move into production responsibly. It is where standards, frameworks, models, and governance meet.

The enterprise future of Java and AI will not be defined by prototypes. It will be defined by platforms that can turn prototypes into systems of record. OpenShift AI is that platform.

For an overview on what's happening at the standards level with Jakarta EE and AI, see the article What's Up With Open Standard Enterprise Java and AI.