1 week Ago By Steven Vaughan-Nichols
Everyone and their dog is investing in AI , but Google has more reason than most to put serious effort into its offerings. As Google CEO Sundar Pichai said in an internal meeting before last year's holidays: "In 2025, we need to be relentlessly focused on unlocking the benefits of [AI] technology and solve real user problems." Also: The most popular AI tools of 2025 (and what that even means) To help realize that vision, at the Google Cloud Next 2025 event in Las Vegas, Google announced substantial advancements in its Kubernetes and Google Kubernetes Engine (GKE) offerings.
These advances aim to empower platform teams and developers to succeed with AI while leveraging their existing Kubernetes skills. Indeed, Gabe Monroy, Google's VP of Cloud Runtimes, said: "Your Kubernetes skills and investments aren't just relevant; they're your AI superpower." Also: The top 20 AI tools of 2025 - and the #1 thing to remember when you use them So, what are those new advances? Let's take a detailed look at the features.
Simplified AI Cluster Management: GKE will offer simplified AI cluster management through tools like Cluster Director for GKE, formerly Hypercompute Cluster . This advance enables users to deploy and manage large virtual machines (VMs) clusters with attached Nvidia GPUs. This feature is particularly beneficial for scaling AI workloads efficiently.
A related service that's on its way is Cluster Director for Slurm . Slurm is an open-source job Linux scheduler and workload manager. The tool manages clusters and schedules jobs for high-performance computing.
Google will use a simplified UI and APIs to provision and operate Slurm clusters, including blueprints for typical workloads with pre-configured software to make deployments reliable and repeatable. Optimized AI Model Deployment: The platform provides optimized AI model deployment capabilities, including the GKE Inference Quickstart and GKE Inference Gateway . These tools simplify the infrastructure selection and deployment of AI models, ensuring benchmarked performance characteristics and intelligent load balancing.
Also: Why delaying software updates could cost you more than you think Monroy said: "We are seeing a clear trend in the age of AI: amazing innovation is happening where traditional compute interacts with neural networks -- otherwise known as 'inference.' Companies operating at the cutting edge of Kubernetes and AI, like LiveX and Moloco , run AI inference on GKE." Cost-Effective Inference: GKE supports cost-effective inference with features like the Inference Gateway.
Monroy said this approach reduces serving costs by up to 30%, cuts latency by up to 60%, and increases throughput by 40% compared to other managed and open-source Kubernetes offerings. We'll have to wait and see if those improvements are realized. Model-aware load balancing is crucial to this strategy.
AI model response length is usually wildly variable from one request to another, so response latency varies widely. Thus, traditional load-balancing techniques like round-robin can break down, exacerbating latency and underutilizing accelerator resources. Instead, the Inference Gateway provides customers with a model-aware gateway optimized with AI-model-aware load balancing, including advanced features for routing to different model versions.
Improved Resource Efficiency: Enhancements also focus on improving resource efficiency, with GKE Autopilot offering faster pod scheduling, scaling reaction time, and capacity right-sizing. This technique allows users to serve more traffic with the same resources or existing traffic with fewer resources. With the improved Autopilot, Google claimed cluster capacity will always be right-sized.
Also: Vint Cerf on how today's leaders can thrive in tomorrow's AI-enabled internet Autopilot currently consists of a best-practice cluster configuration tool and a container-optimized compute platform that automatically right-sizes capacity to match your workloads. What you can't do with this approach is right-size your existing clusters without using a specific cluster configuration. To help, starting in the third quarter, Autopilot's container-optimized compute platform will also be available to standard GKE clusters without requiring a specific cluster configuration.
This option has the potential to be a real winner. AI-enabled Gemini Cloud Assist: Nothing slows down innovation more than diagnosing and debugging a problem in your application. Gemini Cloud Assist provides AI-powered assistance across the application lifecycle, and the company is unveiling the private preview of Gemini Cloud Assist Investigations, which helps users understand root causes and resolve issues faster.
I like this idea a lot. The best part? Assist Investigations will be available right from the GKE console so that you can spend less time troubleshooting and more time innovating. Specifically, it will enable you to diagnose pod and cluster issues from the GKE console -- even across other Google Cloud services, such as nodes, IAM, or load balancers.
Therefore, you can see logs and errors across multiple GKE services, controllers, pods, and underlying nodes. Sign up for the private preview to check this feature out. As part of its broader emerging technology strategy, Google is positioning itself as a leader in AI-optimized platforms, offering businesses a robust foundation for AI-driven transformation.
These developments empower businesses across industries to use AI more effectively, driving innovation and efficiency in operations and customer experiences. Also: Why neglecting AI ethics is such risky business - and how to do AI right For example, Intuit uses Google Cloud's Document AI and Gemini to simplify tax preparation for millions of TurboTax consumers. Reddit uses Gemini via Vertex AI , Google's AI agent builder, to power Reddit Answers , the website's new AI-powered conversation platform, which is meant to help improve the homepage experience.
Can Google pull off these AI-enabled transformations? Stay tuned. As Pichai said in December: "In history, you don't always need to be first, but you have to execute well and really be the best in class as a product. I think that's what 2025 is all about.
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