Red Hat aims to change the economics of application modernization with generative AI

Software drives innovation, but innovation (almost always) comes with complexity. Applications are an excellent example – outdated applications become a bottleneck, hindering innovation and responsiveness to market demands, but rebuilding or refactoring these applications brings an additional level of complexity and can take hundreds if not thousands of person-hours depending on the complexity of the application. But what if you were able to transform your applications source code to more modern frameworks faster?

Today Red Hat is pleased to announce the introduction of generative artificial intelligence (GenAI) to Konveyor, the Red Hat-led open source community project aimed at helping organizations modernize applications to cloud-native technologies.

Embracing application modernization is not just an option but a strategic imperative for enterprises aiming to thrive, not just for today but into the future. Modernization can also facilitate better integration with third-party services and data sources, fostering collaboration and unlocking new growth opportunities. With this in mind, Red Hat has been working upstream with Konveyor, which provides the foundational technologies behind the updated versions of our migration toolkit for applications. Now, Red Hat is working to integrate LLMs into Konveyor to improve the economics of re-platforming and refactoring applications to Kubernetes and cloud-native technologies.

This latest effort is aimed at helping enterprises streamline (and achieve) their modernization goals. GenAI models like IBM watsonx™ Code Assistant, alongside existing knowledge based on additional AI models, can help streamline migrations as a more cost-effective solution when added to the development workflow. With GenAI for application modernization, benefits include:

• Enhancing the developer experience and pipelines with IDE plugins and repository-level automation to enable DevOps pipelines by integrating large-language models (LLMs) into the workflow. This means developers will be provided with recommended source code changes right in their IDE, saving time when refactoring applications;
• Retrieval Augmented Generation (RAG) for leveraging the structured migration data from Konveyor to enable automated source code changes that learn from how your organization tackles modernization, even with your own custom technologies. Each successful transformation of source code further improves the accuracy of future recommendations;
• Expedited development cycles with less human error by utilizing GenAI for better productivity, creativity and efficiency throughout the development process;
• Integration capabilities with other Red Hat application services including Red Hat OpenShift, the industry’s leading hybrid cloud application platform powered by Kubernetes

These new gen AI capabilities will be available for Konveyor later this summer. Red Hat also plans to deliver these capabilities in future releases of our migration toolkit for applications to enhance GenAI models like IBM watsonx™ Code Assistant. To learn more, check out our deep dive into these updates.

The cloud is hybrid. So is AI.

For more than 30 years, open source technologies have paired rapid innovation with greatly reduced IT costs and lowered barriers to innovation. Red Hat has been leading this charge for nearly as long, from delivering open enterprise Linux platforms with RHEL in the early 2000s to driving containers and Kubernetes as the foundation for open hybrid cloud and cloud-native computing with Red Hat OpenShift.

This drive continues with Red Hat powering AI/ML strategies across the open hybrid cloud, enabling AI workloads to run where data lives, whether in the datacenter, multiple public clouds or at the edge. More than just the workloads, Red Hat’s vision for AI brings model training and tuning down this same path to better address limitations around data sovereignty, compliance and operational integrity.  The consistency delivered by Red Hat’s platforms across these environments, no matter where they run, is crucial in keeping AI innovation flowing.