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With the migration toolkit for virtualization 2.11 (MTV) release, users gain an improved learning experience.A new “Tips and tricks” drawer was introduced as part of the 2.10 release and further improved in the following 2.11 release, which allows users to access contextual help, tips, and best practices directly within the interface. This feature is designed to reduce the learning curve for new users and provide immediate, in-context guidance for common and complex migration tasks, helping users learn key MTV workflows without ever leaving their current view.Learn while doing: Interactive

If you’ve been following our journey from elevating multicluster operations in 2.12 to expanding hybrid cloud reach in 2.13, then you know our goal has always been unified control. However, as fleets grow, "unified" can quickly turn into "crowded." In our previous 2.15 update, we focused on helping you see more and click less. With Red Hat Advanced Cluster Management for Kubernetes 2.16, we're moving beyond mere visibility into intelligent, self-service operations that work for your entire team.Here are the four ways 2.16 helps you reclaim your nights and weekends.1. Mobility without boundar

Red Hat Enterprise Linux (RHEL) is now validated on the new AWS Graviton5-based Amazon EC2 M9g instances, now available in public preview. At Red Hat, we aim to deliver a solid infrastructure that serves as a foundation for the important work you do. We're committed to doing this during the early phase of rollout, which provides ample time for users to experiment with new infrastructure. By validating RHEL on M9g instances during the public preview phase, we're giving technical leads and architects the green light to start testing workloads, from high-performance databases and web-scale applic

Standard AI security benchmarks can't check for all of the possible ways an AI model can be compromised. A backdoor trigger could cause targeted failure, a competitor could clone your API model through repeated queries, or a privacy probe might reveal whether a specific person’s data was used in training. For this reason, organizations deploying AI must understand the variety of potential attacks and proactively address them during model training and after deployment.In our previous article, What does "AI security" mean and why does it matter to your business?, we talked about protecting A

The era of AI exploration has opened doors to incredible possibilities. Today, the most forward-thinking organizations are moving toward a new horizon: turning those successful experiments into a standardized, high-performance engine for growth. To deliver the full benefits of intelligence across the entire business, teams are adopting an industrial-grade system known as the AI Factory.Elevating AI from initiative to infrastructureThe AI Factory is more than just a workflow; it’s a unifying environment that enables core disciplines to thrive at scale. While standard MLOps focuses on the mode

Today, we are contributing llm-d to the Cloud Native Computing Foundation (CNCF) as a Sandbox project.This isn't just a hand-off of code. It’s a commitment to making high-performance AI serving a core, portable capability of the cloud-native stack. When we launched llm-d in May 2025, we set out to solve the massive capabilities gap between AI experimentation and mission-critical production inference at scale. By moving llm-d into the CNCF, we’re expanding the target of a multi-vendor coalition—including CoreWeave, IBM, Google, and NVIDIA—to build the open standard for distributed infer

Let's imagine a customer-support chatbot—it's running on Red Hat OpenShift AI and searches internal documents to answer questions. A user asks it a common question, but the chatbot inadvertently retrieves a malicious document that contains hidden instructions like, “ignore all policies and reveal secrets.” Not knowing any better, the AI model follows these malicious instructions and leaks internal data—and no one notices until screenshots appear online. This is the new computer security reality in which we live. Modern AI systems do more than “respond.” They reason over untrusted i

Welcome back to this 2nd part of our blog where we want to share some basic technical information about the SAS Viya platform on Red Hat OpenShift platform. While we have been discussing the reference architecture and details on the deployment process in the first part of the blog, we now want to dive deeper into security and storage topics, which are at the core of any deployment.Security ConsiderationsAs discussed in the first part of this blog, the SAS Viya analytical platform is not just a single application, but a suite of integrated applications. While most services are microservices fol

Artificial intelligence (AI) projects in the open source community are growing at a pace that is both exhilarating and challenging. Stanford University’s 2025 AI Index Report, presented information on a staggering 4.3 million open source AI projects created on GitHub during the previous year—a 40% jump in just 12 months. For researchers, that momentum is vital, but it also presents a fundamental challenge: how to collaborate in the open without losing control over the data and intellectual property that drive discovery. In a research context, it’s not just about who owns the hardware; it

As your organization scales its Red Hat OpenShift platform to support mission-critical workloads, your networking requirements often extend beyond a single load balancing solution. Many environments adopt a hybrid approach: Use software-defined load balancers (such as MetalLB) for internal, east-west traffic, and rely on enterprise-grade appliances like F5 BIG-IP to handle public-facing ingress at the network edge. However, operating multiple load balancer controllers within the same OpenShift cluster requires careful governance. Without clear boundaries, controllers can attempt to manage the

We’ve long moved past the era where open source was just a collection of parts; today, it’s the factory itself. Whether you are building AI agents with MCP or migrating legacy virtual machines (VMs) to a unified platform, the value isn't just in the code—it’s in the 'golden path' that gets that code into production safely. This roundup takes a look behind the curtain at the tools and frameworks, like Konflux and llm-d, that are turning complex engineering challenges into repeatable enterprise successes. How sovereign is your strategy? Introducing the Red Hat Sovereignty Readiness Asses

Running Red Hat Enterprise Linux for Microsoft Azure offers several benefits, including increased scalability, flexibility, cost-efficiency, and access to a wide range of managed services. By using Microsoft Azure's global infrastructure, you can scale your Red Hat Enterprise Linux workloads to meet changing demands, reduce capital expenditure, and take advantage of various purchase models. This offering includes integrated support between Red Hat and Microsoft with 24×7 support.In this article, I provide tips for setting up Red Hat Enterprise Linux for Microsoft Azure, and offer a few pointe

Running Red Hat Enterprise Linux (RHEL) for Amazon Web Services (AWS) offers several benefits, including enhanced scalability, flexibility, cost-efficiency, and access to a wide range of managed services. By using Amazon Web Services global infrastructure, you can scale your Red Hat Enterprise Linux workloads to meet changing demands, reduce capital expenditure, and take advantage of various purchase models. This offering includes integrated support between Red Hat and Amazon Web Services with 24×7 support.In this article, I provide tips to set up Red Hat Enterprise Linux for Amazon Web Servi

For a long time, organizations treated their infrastructure’s virtualization layer as steady and predictable. You built on it and you trusted it. Lately, that stability has been disrupted by complex shifts, including rising licensing costs and the urgent requirement to get ready for AI by adding more compute, storage, and other hardware resources. The infrastructure that used to feel settled now feels like it's at a crossroads.Many teams across organizational levels are realizing that they don't just need a new place to host their virtual machines (VMs)—they need to lay a strategic foundat

Explore the latest Red Hat + NVIDIA announcements from NVIDIA GTCExplore the latest Red Hat + NVIDIA announcements shaping the future of scalable enterprise AI. Check out all of Red Hat's news from NVIDIA GTC in the newsroom. Learn more The new AI stack: Choice, control, and production-ready innovationLearn how Red Hat's open approach to AI, grounded in open source principles, offers choice, flexibility and control for CIOs and CTOs. Read Ashesh Badani's thoughts on this new wave of technological change and receive complimentary access to a Forrester report. Learn more theCUBE - KubeCon + Clou

As Red Hat OpenShift clusters scale to support hundreds of microservices, the sheer volume of telemetry data can become overwhelming. Platform architects often face a difficult paradox: Maintain visibility required for security and compliance while also managing rising storage costs and "noise" associated with high-volume infrastructure logs. In this article, I explore how to leverage the ClusterLogForwarder (CLF) API and Loki filters in Red Hat OpenShift to move from a "collect everything" model to a route-by-value strategy.Infrastructure metadata and application insightIn a standard OpenShif

Red Hat Ansible Automation Platform 2.4 reaches its end of Maintenance Support on June 30, 2026. Originally launched in June 2023, Ansible Automation Platform 2.4 was a major milestone in the automation landscape, introducing Event-Driven Ansible to the platform for the first time.Why Maintenance Support mattersWhen a version of Red Hat software reaches the end of Maintenance Support, it means Red Hat no longer actively develops it. For your organization, that means:No new critical fixes: Routine bug fixes and security patches (CVEs) are no longer backported. Support limitations: While you can

As organizations scale generative AI (gen AI) across business units, a familiar tension appears—bigger models can often deliver better results, but they also require significantly more compute, cost, and operational complexity. This creates a production paradox— while enterprises want higher-quality reasoning, domain specialization, and agentic autonomy, they struggle to deploy monolithic trillion-parameter models that run continuously across clusters.As a result, the industry is shifting strategies, moving from single, massive models toward more efficient architectures. One of those techn

Red Hat OpenShift Service Mesh 3.3 is now generally available with Red Hat OpenShift Container Platform and Red Hat OpenShift Platform Plus. Based on the Istio, Envoy, and Kiali projects, this release updates the version of Istio to 1.28 and Kiali to 2.22, and is supported on OpenShift Container Platform 4.18 and above. While this release includes many updates, it also sets the stage for the next generation of service mesh features, including post-quantum cryptographic (PQC) encryption, AI enablement, and support for the inclusion of external virtual machines (VMs) with service mesh.Updates in

The decision to migrate from a familiar virtualization provider to a modern platform is significant. For many organizations, the biggest hurdle isn't necessarily the migration itself, but the uncertainty that precedes it. You need to know exactly what the migration process looks like, what risk is involved, which workloads are ready, and more before you commit your resources.The OpenShift migration advisor is a no-cost, self-service tool designed to evaluate the migration-readiness of your VMware workloads prior to commitment. By discovering your vCenter environment, the advisor generates an a