Red Hat News

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,

As organizations continue to leverage edge computing for AI workloads running near the source of data generation, Red Hat aims to deliver the enterprise support that companies need to capture the full potential of AI operations at the edge. Following our successful technology preview, we are thrilled to announce that Red Hat Device Edge is now available on NVIDIA Jetson Orin, aligning with the release of Red Hat Enterprise Linux 9.8.During the tech preview phase, we saw tremendous interest from customers wanting to extend AI capabilities to the far edge, whether they are leveraging AI for adva

No enterprise application exists in a vacuum. Applications interact with services, other applications, external resources, databases, and more. As these applications scale, so does the complexity—more containers, more datasets, and more moving parts.At Red Hat, our view is that most developers should not need to manage containers or Kubernetes directly. The most effective approach is to focus on writing business logic and push code to production through a platform like Red Hat OpenShift, using capabilities such as Red Hat OpenShift Dev Spaces and the OpenShift application platform to handle

As AI adoption grows and agents multiply inference demands, a reliable inference foundation becomes critical to running AI initiatives profitably.Today at Red Hat Summit, we are excited to announce that Red Hat AI Inference now runs on any managed Kubernetes service. This expansion enables organizations to leverage a consistent, open inference stack and Kubernetes-native operations wherever they already run their workloads. At launch, we are delivering validated deployment blueprints on 2 platforms: CoreWeave Kubernetes Service (CKS) and Azure Kubernetes Service (AKS). With this release, Red H

Reactive support models can be a liability when a single misconfiguration can stall a global rollout or an essential production upgrade. To address this, Red Hat Support has introduced Red Hat Technical Supportability Review with AI, transforming a reactive hurdle into a proactive advantage.For IT organizations, the future of infrastructure is one of rapid, dynamic evolution and increasing demand. As customers scale Red Hat OpenShift environments to power massive AI-driven workloads, they are frequently managing an unprecedented volume of operational intelligence. At this level of ambition, r

With Red Hat Summit 2026 just around the corner, we’re shifting our focus from 'what’s new' to 'what’s next.' Whether you’re scaling AI or modernizing legacy VMs, this guide highlights the specific sessions and stories that will help you solve your toughest architectural challenges on Microsoft Azure Red Hat OpenShift.If you’re new to the service, Azure Red Hat OpenShift is a fully managed application platform—jointly engineered and operated by Red Hat and Microsoft. This first-party Azure service helps you build, deploy, and manage containerized, virtualized, and AI-enabled applic

At Red Hat, we’ve always believed that the best way to validate true technical skills is through hands-on evaluation of real-world tasks directly aligned with the technologies professionals use every day. That is why we are thrilled to announce a strategic evolution of Red Hat Certification, aligning it more closely with the Red Hat platforms you rely on daily.Whether you are currently a Red Hat Certified Professional looking to maintain your edge, an individual actively pursuing your first certification, or a decision-maker needing transparency to hire top-tier talent, this massive revamp i

It’s no secret – the tech industry is rapidly adopting agentic software development to convert business processes into fully autonomous, agentic workflows. While the power of these tools is undeniable, the current consumption models present a challenge. Most of these solutions are delivered leveraging a model-as-a-service approach that’s poised to trigger an AI version of the cloud paradox: The agentic paradox.The paradox is simple. The fastest path to increase the velocity of your business processes is to use powerful frontier models. However, as adoption scales, this strategy becomes u

As AI compute and storage have evolved to become abstracted, multi-tenant, and automated, AI infrastructure now demands accelerated networking that delivers the same levels of automation, orchestration, and multi-tenancy across fabrics spanning NVIDIA Spectrum-X Ethernet, NVIDIA Quantum InfiniBand, NVIDIA NVLink, and NVIDIA BlueField DPUs. Yet many deployments still rely on fragmented, manually provisioned network environments that struggle to provide the scalability, operational efficiency, and workload isolation required for regulated and sovereign AI deployments. Red Hat is teaming with Net

For teams running virtualization estates of any size today, three pressures are converging at once. Hardware budgets that looked generous in late 2024 have been blindsided by memory costs, turning routine refreshes into financial hurdles. Licensing models have moved in directions most renewal cycles weren't built to absorb. And in many organizations, leadership has moved from "how do we plan the next infrastructure refresh," to "how can we get more out of the infrastructure already in place?" These pressures are not unique to virtualization, but virtualization is where they all show up at once

At Red Hat, we’ve always known that "open" is a team sport. While our technology provides the foundation, our partners act as the ultimate force multipliers—building, integrating, and delivering the solutions that solve the world’s most complex business challenges. Today, we are thrilled to celebrate that spirit of collaboration by announcing the winners of the 2026 Red Hat Ecosystem Innovation Awards. These awards recognize partners who have demonstrated exceptional ingenuity using Red Hat’s open source portfolio to drive measurable, transformative results for our joint customers. Fro