Introducing AI hub and gen AI studio: The new command center for enterprise gen AI in Red Hat OpenShift AI

Introducing AI hub and gen AI studio: The new command center for enterprise gen AI in Red Hat OpenShift AI What's new: A closer look Real-world value for the enterprise How to get started What’s next? The adaptable enterprise: Why AI readiness is disruption readiness About the authors Rob Greenberg Peter Double More like this Blog post Blog post Original podcast Original podcast Keep exploring Browse by channel Automation Artificial intelligence Open hybrid cloud Security Edge computing Infrastructure Applications Virtualization Share The world of gen AI is moving at lightning speed. For enterprises, navigating the flood of new large language models (LLMs), tools (like Model Context Protocol (MCP) servers), and frameworks can feel overwhelming. How do you choose the right model? How do you empower your teams to experiment and build with the latest innovations in AI without creating organizational barriers? At Red Hat, we believe the future of AI is open, accessible, and manageable at scale. That's why we’re excited to announce 2 new consolidated dashboard experiences in Red Hat OpenShift AI 3.0: the AI Hub and the Gen AI studio. These experiences are designed to streamline the entire gen AI lifecycle for enterprises by providing tailored components for the key personas innovating with AI within organizations: platform engineers and AI engineers. AI hub and gen AI studio work together to create a cohesive, end-to-end workflow for building production-ready AI solutions on a trusted, consistent platform. The AI hub is the central point for the management and governance of gen AI assets within OpenShift AI. It empowers platform engineers to discover, deploy, and manage the foundational components their teams need. Key components include: Catalog: A curated library where platform engineers can discover, compare, and evaluate a wide variety of models. This helps overcome "model selection paralysis" by providing the data needed to choose the optimal model for any use case. Registry: A central repository to register, version, and manage the lifecycle of AI models before they are configured for deployment. Deployments: An administrative page to configure, deploy, and monitor the status of models running on the cluster.