LLM Reference
RHEL AI

RHEL AI

Researched 16d agoPlatformTier 3

Red Hat

Hyperscaler

RHEL AI exposes 4 tracked models (0 with output token pricing in seed data). Task coverage across this catalog includes general LLM work; open any model detail page for benchmarks, batch tiers, and migration prompts.

Portfolio context: 0 decision-task tags, 4 catalog rows, latest research stamp 2026-05-19.

Use this portfolio page for

  • Catalog orientation before locking a model SKU

Do not stop here for

  • Final benchmark picks without opening the relevant model detail page
  • Strict price-per-token comparisons until output pricing is sourced

Catalog rows

4

Models linked to this provider in seed data

Priced output routes

0

Add output pricing to unlock comparisons

Cheapest output

Unknown

Need positive token_out rows

Batch-ready SKUs

0

No batch pricing tracked

Latest catalog ship

2024-05-06

759d since dated release field

Freshness

2026-05-19

Researched 16d ago

fresh

Catalog release signal

Latest ISO-dated model.release in this catalog is 2024-05-06 (759d ago).

Where this host wins

Task positioning unavailable until catalog models pick up capability tags or benchmarks.

Getting started

Official entry points from seed metadata — confirm quotas and regions in vendor docs.

Compliance notes (verbatim seed excerpts)

Not yet verified from seed copy — no SOC/ISO/HIPAA-class sentences detected to quote verbatim.

Platform Overview

Red Hat OpenShift AI is a comprehensive platform designed for developing, deploying, and managing AI and machine learning workloads across hybrid cloud environments. It integrates essential tools like TensorFlow, PyTorch, and Jupyter, enabling seamless collaboration between data scientists and developers. The platform facilitates rapid model development, operationalizes AI/ML models using Kubernetes, and supports both small-scale experiments and large-scale production models. With advanced security measures and a cloud-native architecture, it offers flexible deployment options as either a managed service or self-managed software. A standout feature of Red Hat OpenShift AI is its support for retrieval-augmented generation (RAG), allowing users to derive AI insights from their own reference documents. The platform enhances model serving with multi-model server capabilities and distributed workloads, utilizing technologies such as KServe and Ray for efficient data processing. It emphasizes community-driven innovation through open-source principles, enabling enterprises to modernize their applications and infrastructure, and ultimately drive productivity and competitive advantage in the AI landscape.

Compare per-model pricing, input and output token costs, batch availability, and benchmark coverage.

Available Models(4)

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All models available as Serverless

Contact provider for pricing

Platform Details

TypePlatform
TierTier 3
Models4

Organization

Red Hat
Founded1993
Raleigh, North Carolina, United States

Red Hat, a subsidiary of IBM since 2019, is a pioneering technology company that has been at the forefront of open-source software development since its founding in 1993. Known for its commitment to innovation and collaboration, Red Hat has established itself as a leader in providing enterprise-level solutions that leverage the power of open-source communities. The company's portfolio extends beyond traditional software offerings, encompassing a range of products and services that enable organizations to navigate the complexities of modern IT environments, including cloud computing and artificial intelligence. At its core, Red Hat is dedicated to connecting enterprises with cutting-edge open-source technologies, fostering a culture of transparency and meritocracy. The company's influence spans various aspects of the software stack, from operating systems and middleware to advanced cloud solutions. Red Hat's emphasis on hybrid cloud infrastructure and container technologies has positioned it as a key player in the digital transformation landscape, offering businesses the tools they need to enhance agility, efficiency, and innovation in an increasingly complex technological ecosystem.