Microsoft Azure Blog

Summary This post is for technical decision makers evaluating where to run production PostgreSQL workloads. It compares two valid operating models—self-managed PostgreSQL and a managed database service—through business and operational outcomes: control, engineering capacity, resilience, security, cost predictability, risk tolerance, and access to specialist expertise.

This blog post is the second of a four-part series called The Economics of Agent Optimization which shares the strategies, capabilities, and proof points to help you optimize agent costs and run AI as a managed investment system on Microsoft Foundry. The first post set out the three decisions that system rests on: optimize each request at runtime, optimize each workflow over time, and govern spend continuously. This post takes the first, the one that touches every dollar you will ever spend on AI.

For decades, cybersecurity defenders have relied on a relatively straightforward model: a vulnerability is disclosed, security teams assess exposure, test available fixes, deploy patches into production, and ultimately close the risk before attackers can exploit it at scale. That model increasingly reflects a world that no longer exists. Today’s enterprises operate thousands of interconnected workloads across hybrid and multicloud environments.

Microsoft has been named a Leader in the 2026 Gartner® Magic Quadrant™ for Cloud-Native Application Platforms, which we believe recognizes the platforms organizations rely on to build, deploy, and operate cloud-native applications at scale. This is our third consecutive year positioned as a Leader in this report. We are proud of this recognition. More importantly, we believe it reflects a shift we see across industries.

This blog post is the first of a four-part series called The Economics of Agent Optimization which shares the strategies, capabilities, and proof points to help you optimize agent costs and run AI as a managed investment system on Microsoft Foundry. The AI conversation in most enterprises has moved from the whiteboard to the budget review. Two years ago, the question was whether AI could work. The question leaders are asking now is sharper and less comfortable: is it paying for itself?

Microsoft has been named a Leader in the inaugural 2026 Gartner® Magic Quadrant™ for AI-Augmented Code Modernization Tools, which recognizes software solutions that use specialized AI agents, generative AI, and deterministic analysis to accelerate the transformation of legacy systems. We are proud of our placement as a Leader in this first edition of the report.

Every day, customers trust Microsoft Databases to power their most critical applications, business processes, and AI-powered experiences. Continuous customer feedback provides valuable insights into how these technologies perform in production and where we should continue investing.

Takeaway: Brain is Azure’s AI-powered cloud reliability intelligence system: an AIOps system that sits as an intelligent layer on top of Azure Resource Graph and fuses platform telemetry, AI/ML models, service dependencies, and customer impact into a single, continuously updated view of how every service, region, and workload is performing.

Takeaway: Azure Chaos Studio helps organizations validate application resilience by simulating outages, failovers, network disruptions, and infrastructure failures before they impact production. You don’t know with certainty that your application is resilient until that resilience is tested. Better to learn it isn’t by deliberately breaking it in a test environment and watching how it reacts, than by a failure in production.

Claude in Microsoft Foundry is the production path enterprises have been asking for: true frontier model choice, Azure-native controls, simplified procurement, and faster time to value. Most enterprise AI projects do not stall because of model quality. They stall because of everything around the model: procurement, governance, networking, and data. Claude in Microsoft Foundry is now generally available, hosted on Azure, giving teams a faster path from agent experimentation to production.