SubgenAI makes AI practical, scalable, and sustainable with Azure Database for PostgreSQL
Authors: Abe Omorogbe, Senior Program Manager at Microsoft and Julia Schröder Langhaeuser, VP of Product Serenity Star at SubgenAI AI agents are thriving in pilots and prototypes. However, scaling them across organizations is more difficult. A recent MIT report shows that 95 percent of projects fail to reach production. Long development cycles, lack of observability, and compliance hurdles leave enterprises struggling to deliver production-ready agents. SubgenAI, a European generative AI company that focuses on democratizing AI for businesses and governments, saw an opportunity to change this. Its flagship platform, Serenity Star, transforms AI agent development from a code-heavy, fragmented process into a streamlined, no-code experience. Built on Microsoft Azure Database for PostgreSQL, Semantic Kernel, and Microsoft Foundry, Serenity Star empowers organizations to deploy production-grade AI agents in minutes, not months. SubgenAI’s mission is to make generative AI accessible, scalable, and secure for every organization. Whether you're a startup or a multinational, Serenity Star offers the tools to build intelligent agents tailored to your business logic, with full control over data and deployment. “Many things must happen around it in the coming years. Serenity Star is designed to solve problems like data control, compliance, and decision ethics—so companies can unleash the full potential of generative AI without compromising trust or profitability” - Lorenzo Serratosa Simplifying complex AI agent development Technical and operational challenges are inherent in enterprise-wide AI agent deployments. Examples include time-consuming iteration cycles, lack of observability and cost control, security concerns, and data sovereignty requirements. Serenity Star addresses these pain points by handling the entire AI agent lifecycle while providing enterprise-grade security and compliance features. Users can focus on defining their agent's purpose and behavior rather than wrestling with technical implementation details. Its framework focuses on four essentials for AI agents: the brain (underlying model), knowledge (accessible information), behavior (programmed responses), and tools (external system integrations). This framework directly influenced the technology stack choices for Serenity Star, with Azure Database for PostgreSQL powering the knowledge retrieval and Semantic Kernel enabling flexible model orchestration. Real-world architecture in action When a user query comes in, Serenity Star uses the vector capabilities of Azure Database for PostgreSQL to retrieve the most relevant knowledge. That context, combined with the user’s input, forms a complete prompt. Semantic Kernel then routes the request to the right large language model, ensuring the agent delivers accurate and context-aware responses. Serenity Star’s native connectors to platforms such as Microsoft Teams, WhatsApp, and Google Tag Manager are also part of this architecture, delivering answers directly in the collaboration and communication tools enterprises already use every day. Figure 1: Serenity Star Architecture This routing and orchestration architecture applies to the multi-tenant SaaS deployments and dedicated customer instances offered by Serenity Star. Azure Database for PostgreSQL provides native Row-Level Security (RLS) capabilities, a key advantage for securely managing multi-tenant environments. Multi-tenant deployments allow organizations to get started quickly with lower overhead, while dedicated instances meet the needs of enterprises with strict compliance and data sovereignty requirements. Optimizing for scale The same architecture that powers retrieval, routing, and multi-channel delivery also provides a foundation for performance at scale. As adoption grows, the team continuously monitors query volume, response times, and resource efficiency across both multi-tenant and dedicated environments. To stay ahead of demand, SubgenAI actively experiments with new Azure Database for PostgreSQL features such as DiskANN for faster vector search. These optimizations keep latency low even as more users and connectors are added. The result is a platform that maintains sub-60-second response times for 99 percent of chart generations, regardless of deployment model or integration point. With this systematic approach to scaling, organizations can deploy fully functional AI agents that are connected to their preferred communication platforms in just 15 minutes instead of hours. For enterprises that have struggled with failed AI projects, Serenity Star offers not only a secure and compliant solution but also one proven to grow with their needs. Why Azure Database for PostgreSQL is a cornerstone The knowledge component of AI agents relies heavily on retrieval-augmented generation (RAG) systems that perform similarity searches against embedded content. This requires a database capable of handling efficient vector search while maintaining enterprise-grade reliability and security. SubgenAI evaluated multiple vector database options. However, Azure Database for PostgreSQL with PGVector emerged as the clear winner. There were several compelling reasons for this. One is its mature technology, which provides immediate credibility with enterprise customers. Two, the ability to scale GenAI use cases with features like DiskANN for accurate and scalable vector search. There, the flexibility and appeal of using an open-source database with a vibrant and fast-moving community. As CPO Leandro Harillo explains: “When we tell them their data runs on Azure Database for PostgreSQL, it’s a relief. It's a well-known technology versus other options that were born with this new AI revolution.” As an open-source relational database management system, Azure Database for PostgreSQL offers extensibility and seamless integration with Microsoft’s enterprise ecosystem. It has a trusted reputation that appeals to organizations with strict data sovereignty and compliance requirements such as those in healthcare and insurance where reliability and governance are non-negotiable. The integration with Azure's broader ecosystem also simplified implementation. With Serenity Star built entirely on Azure infrastructure, Azure Database for PostgreSQL provided seamless connectivity and consistent performance characteristics. The fast response times necessary for real-time agent interactions are the result, along with maintaining the reliability demanded by enterprise customers. Semantic Kernel: Enabling model flexibility at scale Enterprise AI success requires the ability to experiment with different models and adapt quickly as technology evolves. Semantic Kernel makes this possible, supporting over 300 LLMs and embedding models through a unified interface. With Serenity Star, organizations can make genuine choices about their AI implementations without vendor lock-in. Companies can use embedding models from OpenAI through Azure deployments, ensuring their information remains in their own infrastructure while accessing cutting-edge capabilities. If business requirements change or new models emerge, switching becomes a configuration change rather than a development project. Semantic Kernel's comprehensive connector ecosystem also accelerated SubgenAI's own development process. Interfaces for different vector databases enabled rapid prototyping and comparison during the evaluation phase. “Semantic Kernel helped us to be able to try the different ones and choose the one that fit better for us,” notes Julia Schroder, VP of Product. The SubgenAI team has also extended Semantic Kernel to support more features in Azure Database for PostgreSQL, which is easier because of how well-known and popular PostgreSQL is. SubgenAI has also contributed improvements back to the community. This collaborative approach ensures the platform benefits from the latest developments while helping advance the broader ecosystem. Proven impact of Azure Database for PostgreSQL across industries Because organizations struggle to deliver production-ready agents because of long development cycles, lack of observability, and compliance, the effectiveness of Azure Database for PostgreSQL and other Azure services is reflected in deployment metrics and customer feedback. Production-ready agents typically require around 30 iterations for basic implementations. Complex use cases demand significantly more refinement. One GenAI customer in medical education required over 200 iterations to perfect an agent that evaluates medical students through complex case analysis. Azure PostgreSQL and other Azure services support hour-long iteration cycles rather than week-long sprints, which made this level of refinement economically feasible. Cost efficiency is another significant advantage. SubgenAI provisions and configures models in Microsoft Foundry, which eliminates idling GPU resources while providing detailed cost breakdowns. Users can see exactly how tokens are consumed across prompt text, RAG context, and tool usage, enabling data-driven optimization decisions. Consulting partnerships validate the platform's market position. One consulting firm with 50,000 employees is delighted with the easier implementation, faster deployment, and reliable production performance. Conclusion The combination of Azure Database for PostgreSQL and Semantic Kernel has enabled SubgenAI to address the fundamental challenges that cause 95 percent of enterprise AI projects to fail. Organizations using Serenity Star bypass the traditional barriers of lengthy development cycles, limited observability, and compliance hurdles that typically derail AI initiatives. The platform's architecture delivers measurable results, including a 50 percent reduction in coding time, support for complex agents requiring 200+ iterations, and deployment capabilities that compress months-long projects into 15-minute implementations. Azure Database for PostgreSQL provides the enterprise-grade foundation that customers in regulated industries require, while Semantic Kernel ensures organizations retain flexibility as AI technology evolves. This technological partnership creates a reliable pathway for companies to deploy production-ready AI agents without sacrificing data sovereignty or operational control. Through the reliability of Azure Database for PostgreSQL and the flexibility of Semantic Kernel, Serenity Star delivers an enterprise-ready foundation that makes AI practical, scalable, and sustainable.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.
