SearchCloudComputing

Benefits of Kubernetes for AI The popularity of using Kubernetes for AI workloads didn't come out of nowhere. Kubernetes offers several benefits for uses such as AI model training and inference. One of the key goals of Kubernetes is to allocate CPU, memory and networking resources efficiently. It does this by dynamically deploying workloads across server clusters based on each workload's resource requirements and the available servers with sufficient resources at a given time.

Three planes of control Knowing where data physically resides is no longer enough to guarantee control, said Alistair Macrae, OT security principal at e2e-assure, a managed security services provider. In the pre-AI cloud era, it was enough to keep databases in a specific region. AI breaks this model because data is no longer static. It is dynamically routed, processed through external models and continuously logged.

The cloud is no longer a politically neutral, globally interchangeable utility. Sovereignty laws, sanctions, export controls, procurement restrictions and strategic competition are turning infrastructure choices into business-risk decisions. The cloud's original promises -- global accessibility, centralized infrastructure and economies of scale -- have evolved into an increasingly fragmented environment.

One thing that Kubernetes doesn't do -- at least not natively -- is manage costs. Kubernetes doesn't ensure that the resources available within a cluster are used cost-effectively. Nor does it alert admins to situations where costs may be higher than necessary. If businesses don't monitor costs, they may end up spending much more on deploying containerized applications than necessary.

Although we're three decades into the era of cloud computing, legacy systems are still a common part of enterprise IT landscapes. Finding ways to modernize those systems is important to increase IT efficiency and scalability -- not to mention speeding adoption of new technology, like generative and agentic AI. And while legacy system modernization can be a tough process, there are modernization tools that can help.

Omdia predicts that sales of third-party products through AWS Marketplace, Microsoft Marketplace and Google Cloud Marketplace will reach $163 billion by 2030, up from $45 billion in 2025, growing at 29.1% CAGR. Learn how to maximize your ROI on cloud spend with Red Hat below.

The last thing modern organizations need is to impede agility or interfere with essential daily functions. Legacy systems do exactly that. However, they are more than merely outdated. Common legacy system issues include: Constrains agility, decision-making and speed to market. Increases operational risk and fragility. Prevents businesses from maximizing investment returns on other systems. Locks organizations into inflexible processes and technologies.

Peloton had a problem: Manage peak traffic events -- greater than 450,000 requests per second -- while controlling its AWS infrastructure costs. In 2024, Peloton's engineering team made a bold decision to reduce costs and improve performance. It eliminated the performance environment entirely and chose to test in production instead.

As enterprises accelerate AI deployment, governance frameworks have not kept pace with the systems they are meant to control. Following his session at the BrightTALK Cloud Convergence Summit, thought leader Varun Raj dives deeper into why existing AI governance models are fundamentally misaligned with modern systems and proposes a control-plane approach to restore accountability, reliability, and system-level oversight in production environments.

SAN DIEGO -- AI has become both a challenge and an opportunity for FinOps teams, and AWS used this year's FinOps X conference to showcase a series of product updates that align closely with that reality. AWS introduced the AWS FinOps Agent in public preview, an AI-powered tool designed to automate cloud cost analysis, anomaly investigation and reporting, while surfacing optimization recommendations.