Pavneet Ahluwalia

We're excited to announce the launch of the AKS-MCP Server. An open source Model Context Protocol (MCP) server designed to make your Azure Kubernetes Service (AKS) clusters AI-native and more accessible for developers, SREs, and platform engineers through Agentic AI workflows.

AKS-MCP isn't just another integration layer. It empowers cutting-edge AI assistants (such as Claude, Cursor, and GitHub Copilot) to interact with AKS through a secure, standards-based protocol—opening new possibilities for automation, observability, and collaborative cloud operations.

As engineering teams seek to optimize costs and maintain scalability in the cloud, leveraging Azure Spot Virtual Machines (VMs) in Azure Kubernetes Service (AKS) can help dramatically reduce compute costs for workloads tolerant of interruption.

However, operationalizing spot nodes safely—especially for production or critical workloads—requires deliberate strategies around cluster autoscaling and workload placement.

With the fast-paced advancement of AI workloads, building and fine-tuning of multi-modal models, and extensive batch data processing jobs, more and more enterprises are leaning into Kubernetes platforms to take advantage of its ability to scale and optimize compute resources. With AKS, you can manage up to 5,000 nodes (upstream K8s limit) in a single cluster under optimal conditions, but for some large enterprises, that might not be enough.

At Azure Kubernetes Service (AKS), we deeply recognize how crucial observability is for running stable Kubernetes environments. Given our extensive reliance on Kubernetes internally, we're continually innovating to ensure you have robust, clear, and actionable insights into your cluster health and performance. Observability—the ability to monitor, understand, and manage your systems effectively—is a foundational pillar for AKS product vision to enable our users to achieve more.