Red Hat AI Enterprise
Overview
Red Hat® AI Enterprise is an integrated AI platform for developing and deploying efficient and cost-effective AI models, agents, and applications across hybrid cloud environments. It unifies AI model and application lifecycles to increase operational efficiency, accelerate delivery, and mitigate risk by providing a ready-to-use development environment.
Red Hat AI Enterprise addresses the needs of AI use cases today while accelerating future adoption of new technologies like autonomous workflows and AI agents. With this platform, enterprises can:
- Manage AI model development, training, and tuning across both predictive and generative AI (gen AI) use cases.
- Deploy, manage, and scale AI solutions, including inference, agentic AI workflows, and AI applications.
- Build modern AI applications using familiar tools and frameworks on a single, centralized platform with Kubernetes at its core.
- Deliver a comprehensive, layered approach to security and safety across the entire AI lifecycle.
- Ensure efficient resource allocation and use for model training and inference.
Highlights
- Build and deliver AI applications, models, and agents.
- Get started in less time with ready-to-use development environments, preconfigured tooling, automated deployments, and built-in observability.
- Streamline and automate workflows with intelligent resource allocation and integrated lifecycle management.
- Minimize risk with an enterprise AI stack that is integrated, tested, and supported across any model and any hardware, in hybrid cloud environments.
Features and benefits
Red Hat AI Enterprise provides core capabilities for the full AI lifecycle, including model tuning, high-performance inference, and agentic AI workflow management. It also includes support on any model and any hardware and can be deployed anywhere while meeting data location requirements.
Table 1. Features and benefits of Red Hat AI Enterprise
Feature | Benefit |
Model development | Allows users to experiment with data and build predictive AI models. Offers an interactive Jupyter Lab interface with AI/ML libraries and workbenches. |
Model customization | Simplifies and accelerates model customization, including large language model (LLM) fine-tuning. The Training Hub interface allows the use of private data, and offers continual and reinforcement learning. |
ML, gen AI, and agent Ops | Unifies lifecycle management (MLOps/LLMOps) for predictive, generative, and agentic AI. This reduces complexity and offers centralized control from development to deployment—letting your teams focus on innovation. |
Inference at scale | Is engineered for high-throughput, low-latency model serving. Optimized runtimes (like vLLM and llm-d) meet the unique demands of large foundation models and generative AI applications. |
Catalog and registry | Provides a centralized registry and catalog for accessing and experimenting with validated, optimized models, AI agents, and Model Context Protocol (MCP) servers. |
Safety, monitoring and observability | Provides tools for model monitoring, including performance tracking, data and concept drift detection. Implements AI guardrails so that models remain fair, transparent, and reliable in real-world deployments. |
Resource allocation | Optimizes resource allocation when running models across different graphics processing unit (GPU) platforms and hybrid cloud environments. |
Enterprise platform operations and hybrid cloud consistency
Red Hat AI Enterprise delivers a tested and supported full AI stack, integrated with Red Hat OpenShift® to enhance interoperability and offer business continuity across hybrid cloud environments. The platform delivers a consistent operational model across bare metal, virtual machines, public clouds, and edge locations. This eliminates friction when moving models, supports digital sovereignty, and maximizes infrastructure investments.
Building on a Red Hat AI Enterprise foundation empowers organizations to run, deploy, and scale AI models, providing access to top open source AI technologies on a consistent platform. It standardizes the process of building, deploying, and managing AI workloads and offers workflow integration and access to data sources.
The platform's inherent capabilities can provide advanced operational management (Day 2 operations) for governance, security, and automation. This includes the ability to scale AI workloads across hybrid cloud environments using Kubernetes-native features. Horizontal and GPU scaling with automated resource management helps meet fluctuating demands. This ultimately gives organizations control over where they build, train, and run AI workloads, aligning deployment decisions with regulatory, cost, and strategic goals.
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