<iframe src="https://www.googletagmanager.com/ns.html?id=GTM-5MNKFGM7" height="0" width="0" style="display:none;visibility:hidden">
New call-to-action

Red Hat’s position in the AI landscape - for decision makers

February 25, 2025 @ 4:14 PM

Disclaimer: This personal view on the position of Red Hat within the AI-landscape is based on the personal experience of a specialist solutions architect in application development and AI. 


Red Hat in the AI Landscape: Enabling Enterprise-Ready AI

The AI revolution is transforming industries, and enterprises are looking for scalable, secure, and flexible ways to integrate AI into their operations. Red Hat is uniquely positioned to help businesses leverage AI effectively, thanks to our open-source foundation, hybrid cloud capabilities, and enterprise-grade solutions. With the recent acquisition of Neural Magic, we’re taking AI performance to the next level by optimising deep learning models to potentially run efficiently on standard CPUs, reducing hardware dependencies and lowering costs. Neural Magic’s sparse inferencing technology (AI inferencing, similar to an application server for AI models, where trained models make real-time predictions) accelerates AI workloads without the need for GPUs, leveraging software-driven optimisations. Additionally, its compression algorithms significantly reduce model sizes while maintaining accuracy, enabling faster and more efficient AI inferencing across diverse environments, from cloud to edge computing.

 

The Power of Open, Hybrid, and Multi-Cloud AI

AI workloads are complex, requiring robust infrastructure, data pipelines, and security. Red Hat OpenShift, our leading hybrid and multi-cloud application platform, ensures that AI models, applications integrating AI capabilities, and data pipelines can run anywhere: on-premises, in the cloud, or at the edge. This flexibility is crucial for businesses that need to balance performance, cost, and compliance while deploying AI at scale.

 

Agile AI Development with OpenShift

AI is not a one-time investment; it’s an iterative process that requires rapid experimentation, deployment, and scaling. OpenShift streamlines AI development, making it easier for teams to integrate AI models into applications while leveraging containerisation and Kubernetes-based orchestration. It enables agile development at the application, data, and AI levels. As an example, if you have a predictive engine for ice cream salons and data scientists need new

APIs integrated (e.g., weather APIs), you don’t want to tell them, "It will be there in the next iteration within half a year." That delay would hinder efficiency and slow down innovation.

Our approach is based on enterprise open source, ensuring innovation without vendor lock-in, which is critical as AI companies currently bear e.g., the electricity costs, but these expenses will soon shift to enterprises. Organisations must be able to adapt quickly, as prices will fluctuate.

Additionally, new AI products are not always released simultaneously across all cloud providers. If a crucial technology is only available in Cloud X or if GPU shortages occur, businesses need the flexibility to migrate seamlessly, and with full community support.

 

Security and Compliance: AI Without Compromise 

AI introduces new security challenges, from data privacy to model integrity. Red Hat embeds enterprise-grade security across the stack, from the OS to Kubernetes, ensuring compliance with industry regulations. Our commitment to supply chain security, zero-trust principles, and automated security policies helps enterprises deploy AI confidently in sensitive environments.

 

takeonAI1

Gartner: Access rights, data security, is a big concern and a challenge: who can access which data? You can only implement it properly if the underlying infrastructure and components are built with security in mind and when your AI-architecture allows data security.

Additionally, OpenShift provides the capability to easily implement demilitarised zones (DMZs) and service meshes, further enhancing security by segmenting workloads, enforcing policies, and ensuring controlled communication between AI applications and data pipelines.

 

Building AI-Driven Data Pipelines 

AI thrives on data, and Red Hat’s middleware portfolio, including Kafka, Camel, Service Mesh, and Serverless, enables real-time, scalable data pipelines. These technologies allow businesses to ingest, process, and serve AI-driven insights efficiently. For example, Kafka’s event-driven architecture supports AI-powered decision-making by enabling continuous data streaming, which is critical for applications like fraud detection and predictive maintenance.

 

takeonAI2

Gartner: Don't forget data management/date pipelines within your AI-story.

Flexible AI Implementation: Adapt to Your Needs 

With Red Hat’s middleware portfolio, OpenShift, OpenShift AI, and Quarkus, enterprises can build and evolve AI implementations to fit their needs. Whether you require retrieval-augmented generation (RAG), Agentic AI, ML model design and builds, fine-tuning, function calling, or other AI techniques, Red Hat provides a robust foundation.
 
This flexibility ensures that businesses can align their AI strategies based on internal data science capabilities and security requirements.
 

Enterprise AI with a Strong Ecosystem

Red Hat’s open ecosystem extends AI beyond model training, integrating with databases, big data solutions, and data lakes. Whether you need to connect AI workloads with SAP, IBM, NVIDIA, or Snowflake, our partnerships ensure that enterprises can build a fully integrated AI strategy.

 

Local AI Models and instructLab: Full Control and Cost Predictability 

As AI adoption grows, enterprises are increasingly concerned about data control, cost predictability, and adaptability. Local AI models ensure that businesses retain full control over their critical data, mitigating risks even when enterprise contracts are in place. Unlike

cloud-based models that require continuous data input for improvements, local models eliminate concerns about unintended data usage. Moreover, local AI models provide cost predictability, avoiding unexpected spikes in usage-based billing, a common challenge with online models like OpenAI.

takeonAI3Gartner: It's easy to waste money with AI.

Red Hat enables enterprises to manage local AI models via RHEL AI or OpenShift AI, which do not necessarily have to run on-premises but can also be deployed in cloud environments, such as AWS or Azure managed services. This flexibility ensures that businesses can leverage AI while maintaining operational control and cost efficiency.

takeonAI4

Gartner: When you understand what’s happening with AI and when you are in control, you can save a tremendous amount of money.

Additionally, InstructLab allows enterprises to fine-tune AI models without requiring data science expertise. This open-source tool empowers domain experts and developers to refine model behaviour efficiently, accelerating AI adoption without the need for specialised ML skills.

 

Neural Magic: Unlocking AI Performance at Scale 

With Neural Magic, Red Hat is democratising AI performance by allowing businesses to run deep learning models on commodity hardware. This means enterprises can achieve

high-performance AI without expensive GPUs, significantly reducing costs while maintaining efficiency. This advancement is particularly beneficial for organisations deploying AI in edge computing environments, where specialised hardware is often impractical.

 

Why Red Hat for AI?

  • Run AI anywhere: Hybrid and multi-cloud flexibility with
  • Enterprise security: Trusted, secure, and compliant AI
  • Scalable data pipelines: Real-time data movement for AI-driven
  • Optimized AI performance: Neural Magic’s software-driven approach to deep learning acceleration.
  • Open-source innovation: Enterprise-grade AI without vendor lock-
  • Strong partner ecosystem: Seamless integration with databases, big data, and analytics platforms.
  • Full control with local AI models: Ensure data security and predictable costs with RHEL AI and OpenShift AI.
  • Easy AI model fine-tuning: InstructLab enables businesses to refine AI without data science expertise.
  • Flexible AI implementation: Adapt AI strategies based on your data science skills and security requirements using OpenShift, OpenShift AI, Quarkus, and Red Hat

AI is the future of business, and Red Hat ensures it’s enterprise-ready. Whether you are scaling AI across your organisation or exploring new AI-driven use cases, we provide the platform, security, and flexibility to make it happen.

Let’s build the future of AI together.

-

No Comments Yet

Let us know what you think

Subscribe by Email