AI

Running AI Workloads on Azure Kubernetes Service: From Platform Control to Model Serving Most teams begin their AI journey by using a managed model API. This approach works initially but can become problematic when costs become unpredictable at scale, data sovereignty or compliance issues prevent sending data to third-party endpoints, or the team needs a fine-tuned model or a custom inference runtime. At this stage, the focus shifts from simply consuming AI to actively operating it. ...

Part 1 introduced AppNet, while Part 2 demonstrated its use in zero-trust and multi-cluster patterns. This concluding section explores a shared AI gateway, the operational signals derived from the mesh, and the architectural tradeoffs that influence whether AppNet should be integrated into your platform. Series navigation Part 1: What AppNet is and how to get started Part 2: Zero-trust and multi-cluster patterns Part 3: AI gateway, observability and production fit Example three: a shared AI gateway that rate-limits by application AI Gateway with Per-Application Token Rate Limiting] ...

Azure Kubernetes Service (AKS) has long been the leading platform for containerized workloads on Microsoft Azure. While AKS offers scalability and resilience, managing it day-to-day still involves switching between various tools: Azure CLI, kubectl, ARM templates, and portal dashboards. This is where the Microsoft AKS-MCP Server, part of the Model Context Protocol (MCP) ecosystem, makes a difference. By providing a structured, secure interface for agents and tools to perform Azure and Kubernetes actions, AKS-MCP changes how engineers interact with clusters. ...