Build Smarter with Azure HorizonDB
By: Maxim Lukiyanov, PhD, Principal PM Manager; Abe Omorogbe, Senior Product Manager; Shreya R. Aithal, Product Manager II; Swarathmika Kakivaya, Product Manager II Today, at Microsoft Ignite, we are announcing a new PostgreSQL database service - Azure HorizonDB. You can read the announcement here, and in this blog you can learn more about HorizonDB’s AI features and development tools. Azure HorizonDB is designed for the full spectrum of modern database needs - from quickly building new AI applications, to scaling enterprise workloads to unprecedented levels of performance and availability, to managing your databases efficiently and securely. To help with building new AI applications we are introducing 3 features: DiskANN Advanced Filtering, built-in AI model management, and integration with Microsoft Foundry. To help with database management we are introducing a set of new capabilities in PostgreSQL extension for Visual Studio Code, as well as announcing General Availability of the extension. Let’s dive into AI features first. DiskANN Advanced Filtering We are excited to announce a new enhancement in the Microsoft’s state of the art vector indexing algorithm DiskANN – DiskANN Advanced Filtering. Advanced Filtering addresses a common problem in vector search – combining vector search with filtering. In real-world applications where queries often include constraints like price ranges, ratings, or categories, traditional vector search approaches, such as pgvector’s HNSW, rely on multiple step retrieval and post-filtering, which can make search extremely slow. DiskANN Advanced Filtering solves this by combining filter and search into one operation - while the graph of vectors is traversed during the vector search, each vector is also checked for filter predicate match, ensuring that only the correct vectors are retrieved. Under the hood, it works in a 3-step process: first creating a bitmap of relevant rows using indexes on attributes such as price or rating, then performing a filter-aware graph traversal against the bitmap, and finally, validating and ordering the results for accuracy. This integrated approach delivers dramatically faster and more efficient filtered vector searches. Initial benchmarks show that enabling Advanced Filtering on DiskANN reduces query latency by up to 3x, depending on filter selectivity. AI Model Management Another exciting feature of HorizonDB is AI Model Management. This feature automates Microsoft Foundry model provisioning during database deployment and instantly activates database semantic operators. This eliminates tens of setup and configuration steps and simplifies the development of new AI apps and agents. AI Model Management elevates the experience of using semantic operators within PostgreSQL. When activated, it provisions key models for embedding, semantic ranking and generation via Foundry, installs and configures the azure_ai extension to enable the operators, establishes secure connections, integrates model management, monitoring and cost management within HorizonDB. What would otherwise require significant manual effort and context-switching between Foundry and PostgreSQL for configuration, management, and monitoring is now possible with just a few clicks, all without leaving the PostgreSQL environment. You can also continue to bring your own Foundry models, with a simplified and enhanced process for registering your custom model endpoints in the azure_ai extension. Microsoft Foundry Integration Microsoft Foundry offers a comprehensive technology stack for building AI apps and agents. But building modern agents capable of reasoning, acting, and collaborating is impossible without connection to data. To facilitate that connection, we are excited to announce a new PostgreSQL connector in Microsoft Foundry. The connector is designed using a new standard in data connectivity – Model Context Protocol (MCP). It enables Foundry agents to interact with HorizonDB securely and intelligently, using natural language instead of SQL, and leveraging Microsoft Entra ID to ensure secure connection. In addition to HorizonDB this connector also supports Azure Database for PostgreSQL (ADP). This integration allows Foundry agents to perform tasks like: Exploring database schemas Retrieving records and insights Performing analytical queries Executing vector similarity searches for semantic search use cases All through natural language, without compromising enterprise security or compliance. To get started with Foundry Integration, follow these setup steps to deploy your own HorizonDB (requires participation in Private Preview) or ADP and connect it to Foundry in just a few steps. PostgreSQL extension for VS Code is Generally Available We’re excited to announce that the PostgreSQL extension for Visual Studio Code is now Generally Available. This extension garnered significant popularity within the PostgreSQL community since it’s preview in May’25 reaching more than 200K installs. It is the easiest way to connect to a PostgreSQL database from your favorite editor, manage your databases, and take advantage of built-in AI capabilities without ever leaving VS Code. The extension works with any PostgreSQL whether it's on-premises or in the cloud, and also supports unique features of Azure HorizonDB and Azure Database for PostgreSQL (ADP). One of the key new capabilities is Metrics Intelligence, which uses Copilot and real-time telemetry of HorizonDB or ADP to help you diagnose and fix performance issues in seconds. Instead of digging through logs and query plans, you can open the Performance Dashboard, see a CPU spike, and ask Copilot to investigate. The extension sends a rich prompt that tells Copilot to analyze live metrics, identify the root cause, and propose an actionable fix. For example, Copilot might find a full table scan on a large table, recommend a composite index on the filter columns, create that index, and confirm the query plan now uses it. The result is dramatic: you can investigate and resolve the CPU spike in seconds, with no manual scripting or guesswork, and with no prior PostgreSQL expertise required. The extension also makes it easier to work with graph data. HorizonDB and ADP support open-source graph extension Apache AGE. This turns these services into fully managed graph databases. You can run graph queries against HorizonDB and immediately visualize the results as an interactive graph inside VS Code. This helps you understand relationships in your data faster, whether you’re exploring customer journeys, network topologies, or knowledge graphs - all without switching tools. In Conclusion Azure HorizonDB brings together everything teams need to build, run, and manage modern, AI-powered applications on PostgreSQL. With DiskANN Advanced Filtering, you can deliver low-latency, filtered vector search at scale. With built-in AI Model Management and Microsoft Foundry integration, you can provision models, wire up semantic operators, and connect agents to your data with far fewer steps and far less complexity. And with the PostgreSQL extension for Visual Studio Code, you get an intuitive, AI-assisted experience for performance tuning and graph visualization, right inside the tools you already use. HorizonDB is now available in private preview. If you’re interested in building AI apps and agents on a fully managed, PostgreSQL-compatible service with built-in AI and rich developer tooling, sign-up for Private Preview: https://aka.ms/PreviewHorizonDB.Build AI-Ready Apps and Agents with PostgreSQL on Azure
As developers, we’re constantly looking for ways to build smarter, faster, and more scalable applications. The Microsoft Reactor series, Build AI apps with Azure Database for PostgreSQL, is a four-part livestream experience designed to help you do just that—by combining the power of PostgreSQL with Azure’s AI capabilities. Dive into the world of AI apps and agents with Azure Database for PostgreSQL in this engaging video series—your ideal starting point for building intelligent solutions and improving your workflow. Get ready to explore the fundamentals of AI and discover how vector support in databases can elevate your applications. Uncover how innovative tools like the Visual Studio Code extension for PostgreSQL and GitHub Copilot can make your database work faster and more efficient. You'll also see how to create intelligent apps and AI agents using frameworks such as LangChain and Semantic Kernel. Why This Series Matters PostgreSQL is already a favorite among developers for its flexibility and open-source strength. But when paired with Azure’s AI services, it becomes a launchpad for intelligent applications. This series walks you through how to: Orchestrate AI agents using PostgreSQL as a foundation. Enhance semantic search with vector support and indexes like DiskANN. Integrate Azure AI services to enrich your data and user experiences. Boost productivity with tools like the Visual Studio Code PostgreSQL extension and GitHub Copilot. What You'll Learn Each session is packed with practical insights: Episode 1: Laying the foundation: AI-powered apps and agents with Azure Database for PostgreSQL We introduce key AI concepts, setting the stage for a deeper understanding of Large Language Models (LLMs) and its applications, we will explore the capabilities of Azure Database for PostgreSQL, focusing on how its vector support enables advanced semantic search through technologies like DiskANN indexes. We'll also discuss the Azure AI extension, which brings powerful AI features to your data projects, helping you enrich your applications with enhanced search relevance and intelligent insights, and provide a solid foundation for leveraging these tools in your own solutions. Register here Episode 2: Accelerate your data and AI tasks with the VS Code extension for PostgreSQL and GitHub Copilot This talk will delve into how the Visual Studio Code extension for PostgreSQL can streamline your database management, while GitHub Copilot's AI-powered assistance can boost your productivity. Learn how to seamlessly integrate these tools to enhance your workflow, automate repetitive tasks, and write efficient code faster. Whether you're a developer, data scientist, or database administrator, this session will provide you with practical insights and techniques to elevate your data and AI projects. Join us to learn how to effectively use these advanced tools and take your data skills to the next level. Register here Episode 3: Build your own AI copilot for financial apps with PostgreSQL Join us to discover how to transform traditional financial applications into intelligent, AI-powered solutions with Azure Database for PostgreSQL. In this hands-on session, you'll learn to integrate generative AI for high-quality responses to financial queries using PDF-based Statements of Work and invoices, perform AI-driven data validation, apply the Azure AI extension, implement vector search with DiskANN indexes, enhance results with semantic re-ranking, use the LangChain framework, and leverage GraphRAG on Azure Database for PostgreSQL. By the end, you’ll have gained practical skills to build end-to-end AI-driven applications using your own data and projects. Register here Episode 4: Build advanced AI Agents with PostgreSQL Using a sample dataset of legal cases, we’ll show how AI technologies empower intelligent agents to provide high-quality answers to legal queries. In this session, you’ll learn to build an advanced AI agent with Azure Database for PostgreSQL, integrating generative AI for enhanced data validation, retrieval-augmented generation (RAG), semantic re-ranking, Semantic Kernel, and GraphRAG via the Apache AGE Graph extension. This practical demonstration offers insights into developing robust, intelligent solutions using your own data. Register here Join us for an inspiring and hands-on experience—don’t miss out! Get the full series details and register now: https://aka.ms/postgres-ai-reactor-series
