Red Hat News

Recently at Red Hat Summit 2026, Amazon Web Service (AWS) showcased the breadth and depth of its nearly 20-year collaboration with Red Hat, spanning agentic AI development, AI inference optimization, open source infrastructure innovation, and expanded Red Hat software availability on AWS Marketplace. For the engineers, architects, and technology leaders who have been building on Red Hat platforms and AWS infrastructure, the latest milestones in our collaboration have the potential to materially change how they develop, deploy, and operate workloads—and how much it costs to do so.Kiro and Red

It seems there’s a new product or software springing up in the tech world all the time. What’s more, you’re often expected to become your company’s de facto expert and you only have one month to do it. One week. One day. By this afternoon. Whether it be for a personal project, or for a company-wide transformation, learning new tools can take time and bandwidth that you don’t have. Sometimes, simply finding the correct documentation or piece of content that makes sense to you is a challenge by itself. Everyone has different ways to learn and time available to do it, so what’s the so

Open source AI is more exciting than ever, offering a growing number of AI models to choose from. There are reasoning-heavy architecture options like DeepSeek and Kimi K2, more versatile options like the highly-adopted Qwen family, and the specialized, edge-ready Granite 4 from IBM. Having this many options is great news, but it can make users feel paralysis by analysis when trying to decide which model fits their project best.For many organizations, the challenge is no longer finding an open source model, but identifying which will perform best given their specific constraints, whether that i

More than 10 years ago, Red Hat began a collaboration with Microsoft rooted in a simple but powerful recognition: enterprise customers need trusted, interoperable platforms that offer true choice. What started as a shared commitment to openness has grown into a deepening alliance that has guided thousands of organizations as they build, run, and scale workloads across on-premises environments and Microsoft Azure. The goal remains consistent: provide organizations the flexibility to move at their own pace. This includes digital sovereignty, which extends beyond data location to operational cont

Catch up on the news at Red Hat Summit 2026This definitive guide to all of Red Hat's news offers a centralized view of the latest product innovations, strategic collaborations, and visionary milestones. Learn more Watch the Red Hat Summit keynotesHear how Red Hat's unified platform—spanning infrastructure, sovereignty, and intelligence—gives organizations the foundation to move fast with AI without betting on the wrong technology, the wrong vendor, or the wrong moment. The future isn't one cloud or one model. It’s the right platform. Learn more Fierce Network - Red Hat: AI’s foundat

Discover how Red Hat OpenShift AI 3.4 and Red Hat Connectivity Link deliver Models-as-a-Service (MaaS) to centrally govern and scale enterprise AI model serving.Many enterprises have moved past the AI pilot phase. Models are running in production and teams are consuming them, but now they're hitting the governance wall. Who controls which team can access which model? Who approved that inference endpoint for customer-facing use? What does the organization owe to a compliance team asking for usage reports?The answer depends on your starting point. Many organizations are building their AI inferen

We’re already looking forward to the next milestone in open source collaboration. For 2027, Red Hat Summit is returning to its roots in the heart of the East Coast’s tech corridor—Boston, MA. We invite you to clear your schedule for Red Hat Summit 2027, taking place May 25-27, 2027.Our annual gathering serves as the global focal point for the open source community. We look forward to welcoming thousands of customers, partners, and industry pioneers for a week dedicated to pushing the boundaries of what’s possible through collaboration and shared code.Red Hat Summit 2027 is the premier

As organizations move from AI experimentation toward scalable production, the complexity of manually building high-performing AI models and retrieval systems often creates a significant bottleneck. Red Hat OpenShift AI 3.4 helps address this with the introduction of AutoML and AutoRAG, now available as technology previews. These tools are designed to automate the labor-intensive aspects of the AI lifecycle, allowing teams to focus on business impact rather than manual configuration.What is AutoML?AutoML in Red Hat OpenShift AI 3.4 brings enterprise-grade automated machine learning to your fing

Modern large language models (LLMs) are defined by their scale. GPT-3 introduced 175 billion parameters in 2020, and today, production-grade models routinely operate in the hundreds of billions, with some architectures exceeding one trillion. Each parameter represents a learned weight, collectively encoding the language, reasoning, and knowledge that make these systems capable.This scale is not incidental. Empirical research has consistently demonstrated that model capability scales predictably with size—larger models exhibit stronger reasoning, broader factual recall, and greater generaliza

Agentic AI doesn’t just move AI forward, it flips the infrastructure built for traditional inference on its head. Agentic AI, systems that reason, plan, use tools, and execute multistep tasks autonomously, is rapidly moving from research into production. This shift is not just about more compute, it fundamentally changes how infrastructure must perform, scale, and be optimized for continuous, multistage reasoning workflows.But agents are fundamentally different workloads—they call models repeatedly, fan out across tools and data sources, and need to run continuously and cost-effectively. T

Modern IT is driven just as much by economic decisions as it is by technical concepts. The costs of hardware, cloud services, storage, and more all factor into how CIOs and IT leaders budget and deploy their strategies. And now with AI, we have another cross-disciplinary decision to incorporate: Token economics, in this case, how our AI strategies consume the volatile costs of underlying AI models.Currently, most enterprise AI relies on calling frontier model APIs and paying for tokens consumed and generated. While this is an easy starting point, the math is changing. Token consumption is skyr

The past few years have been a period of significant change and challenges in the virtualization industry. Customers are forced to re-evaluate their virtualization platforms while adapting to the rapid rise of AI and infrastructure as well as supply chain pressures.Even with all this turmoil, technology leaders and virtualization admins still need to manage the critical workloads, databases, applications, and virtual machines (VMs) within the budget constraints. If you’re wondering what's next for virtualization infrastructure, the answer is Red Hat OpenShift Virtualization. This strategic e

If you ship software in containers, you know the vulnerability treadmill: Scanners surface a flood of CVEs, backlogs swell, and teams chase patch velocity as if it were the core business of the company (as opposed to serving customers and stakeholders). Complicating matters further is when a lengthy scan result fails to answer the key question that matters: Which of these findings would materially change our risk if we fixed them now?Much of that added load and increased pressure is noise. Results contain findings tied to packages that never run, paths that are not reachable, or components tha

We’re continuing to navigate a fundamental shift in digital infrastructure. Over the past 18 months, the predictability of the virtualization layer has shed nearly 20 years of stability driven by an unrelenting cost crisis. But this is just a symptom of a much deeper architectural challenge. The reality is that the infrastructure layer is being asked to perform tasks it was never originally designed for. We aren’t just managing virtual machines or even virtual machines with containers; it’s a simultaneous balancing act between legacy systems, cloud-native apps, and the burgeoning, GPU-in

If you watched the day one keynote at Red Hat Summit 2026, you heard me talk about critical inflection points that have defined the IT industry. Linux and Kubernetes formed two big waves in recent memory, and AI is undeniably causing another shift. Each of these events was disruptive, but the seismic impact of AI continues to resonate as we face a widening gap between environmental complexity and the flat resources available to manage it. Many of us are being asked to launch new ambitious AI initiatives while simultaneously maintaining the legacy systems the business depends on. To bridge this

The age of generative AI brings great opportunity and new risks. As large organizations adopt agentic AI and face increasingly strict digital sovereignty requirements, the underlying infrastructure must provide integrity, isolation, and identity management to protect sensitive data, workloads, and end users. Protecting organizational reputation, maintaining and building trust in the software we interact with every day is not just a secondary requirement, but the fundamental mission.Imagine a regional public utility responsible for keeping the lights on for millions of homes, schools, and hosp

Artificial intelligence (AI) workloads are transforming industries from financial services to healthcare. However, the use of AI models introduces risk around protecting models, weights, and data from malicious actors. While the industry has established robust traditional security frameworks to protect data at rest (with disk encryption, such as LUKS) and data in transit (with encrypted communication channels like TLS), a gap remains around data that's in use.When sensitive data, such as patient medical records or proprietary AI model weights are actively loaded into the CPU, GPU, and memory f

You've tried it. You've pulled a Model Context Protocol (MCP) server from a GitHub repo, wrestled it into a container, sorted out authentication yourself, and hoped it would hold up in production. That is the state of MCP adoption in the enterprise today—promising protocol, but painful deployment.Red Hat OpenShift AI 3.4, part of the Red Hat AI portfolio, takes a different approach. We're introducing the MCP catalog (now in developer preview): a curated catalog of MCP servers that you can discover, deploy, and manage directly on Red Hat OpenShift. It ships pre-loaded with MCP servers from Re

As enterprises move from the hype of generative AI to building governed, production-ready AI applications, several new challenges have emerged. Currently, many businesses route all of their prompts to massive, cloud-based LLMs, which can result in excessive costs, high latency, and unnecessary data exposure. To solve this problem, Red Hat in collaboration with NVIDIA is bringing enterprise-grade AI development directly to the developer’s desk. We are excited to announce the development preview of Red Hat Enterprise Linux 10 (RHEL 10) on NVIDIA DGX Spark. Powered by the NVIDIA GB10 Grace Blac

Over the last year, Red Hat and HashiCorp have collaborated to create integrations that bring together infrastructure automation and secrets management. Our existing Red Hat Ansible Certified Content Collections have enabled organizations to use HashiCorp Vault, HashiCorp Terraform, and Red Hat Ansible Automation Platform for infrastructure automation and secrets management.HashiCorp Vault integration for secrets managementThe Red Hat Ansible Certified Content Collection for HashiCorp Vault has enhanced how teams handle sensitive credentials in automated workflows. With version 1.0 and beyond,