Introducing Azure HorizonDB - PostgreSQL
Call AI models directly from SQL, build durable vector pipelines inside the database, and deliver high-accuracy similarity search at massive scale with DiskANN and AI re-ranking, all without leaving PostgreSQL. Debug and optimize queries faster with the Azure HorizonDB VS Code extension. Visualize execution plans, let Copilot generate fixes, and clone production data to test environments in seconds. Charles Feddersen, PostgreSQL Partner Director PM, shares how to put all of it to work on Azure. Same hardware, same zone, same PostgreSQL version. 4,200 TPS self-managed vs. 11,000+ on Azure HorizonDB. The separation of storage and compute layers make the difference. See how it works. Chunking. Embeddings. Vector storage. All running as a durable background task inside PostgreSQL with AI Pipelines in Azure HorizonDB, no external orchestration needed. Check it out. Visual query execution plans. Copilot-generated fixes. The Azure HorizonDB VS Code extension brings all of it into the editor. Get it now. QUICK LINKS: 00:00 — Azure HorizonDB features 00:57 — Open-source PostgreSQL 02:24 — How it works 03:37 — Performance 04:51 — Enterprise-ready security 05:34 — Memory & storage work together 06:29 — AI Model Management + AI Functions 08:24 — AI Pipelines 09:50 — DiskANN + AI Re-ranking 10:50 — VS Code Extension + Data Cloning 12:31 — Wrap up Link References Check out our blog at https://aka.ms/azurepostgresblog Unfamiliar with Microsoft Mechanics? As Microsoft’s official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft. 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Now, we’ll go deep on its ultra fast performance at high scale, the built-in resiliency across different availability zones, along with major new built-in AI-centric features, leveraging DiskANN, as well as integrated AI model management, where you can provision models or bring your own from Microsoft Foundry, plus a built-in AI pipeline that automatically chunks and processes data in real time as it’s ingested into the database. And finally, a brand new VS code extension with AI-powered query development and debugging as you build your apps and more. And today I’m joined once again by Charles Feddersen who leads the Postgres efforts on Azure. Welcome back to the show. - Thanks Jeremy, it’s good to be back and we’ve got a lot to get through today. - Yeah, so the new HorizonDB service in Azure is our newest managed Postgres service where we also have Azure database for Postgres, and we’re actively making deep contributions in the Postgres project as well. - Yeah, so Microsoft has been actively supporting Postgres on Azure for about nine years now, and our approach has always been to keep the Postgres, you know, and love for its portability and ecosystem and make Azure the best place to run your Postgres workloads. You know, and as you mentioned, we are a major contributor to open source Postgres. In Postgres 19, Microsoft Committers modified over 64,000 lines of code, which represents about 8% of all changes in this version. And we made about 340 commits. These improvements cover both new functionality and also improvements based on our own learnings of running Postgres at a massive scale on Azure. We also host the largest virtual Postgres community conference in the world called POSETTE, which is now in its fifth year. Because we understand the core Postgres engine so well, it’s not unusual for our core committers on the team to work with our customers to help debug and resolve issues that they might be having as well. - Right, and I remember last time you were on, we discussed how you were shipping major versions of Postgres on Azure within weeks of their releases. - Yeah, and now we’ve effectively eliminated that delay. There is now zero cloud lag in waiting to leverage the latest features when using Postgres on Azure. We ship the new major version on the same day in Azure. - So that’s a big deal, there’s no lag, same day access. Why don’t we move back though to our newest managed Postgres service HorizonDB. How does that then change the game for anyone who’s running Postgres services now on Azure? - Yeah, so HorizonDB brings cloud scale performance built-in resilience and AI ready capabilities to Postgres without changing how you build or run your apps. How this works is we’ve completely decoupled compute and storage to improve performance, scale and availability. At the compute layer, it runs the Postgres engine, fully compatible so existing apps and tools just work. At the storage layer, we built a new and highly optimized log service for transactions where we can sustain a commit latency of typically under one millisecond. That log service is paired to a new shared storage platform, which natively stores data across availability zones for resilient storage by default. We can attach multiple read replicas to the storage and the primary can fail over to any of these replicas in less than five seconds, in the event of an outage. This gives you read scale without replication latency. Ultimately, HorizonDB lets you run any Postgres workload from new AI apps to large scale enterprise systems with the performance, resilience and scale of Azure already built in. - All right, so you said performance. Let’s dig deeper there. Everybody loves a performance story, so can you prove it? - Yeah, so one of the big challenges with self-managed Postgres is that enabling high availability, especially across cloud zones, often comes with a performance cost. With HorizonDB, we introduced a new quorum commit protocol that let you durably commit across zones before flushing to disk, so you get both resilience and high performance. Now to show this in action, I’ve got a split screen, HorizonDB on the right and self-managed Postgres on the left. This is the same Postgres version on the same hardware with the same high availability setup in the same Azure region. And I’ll kick both off and then go ahead and let them run. And as you can see clearly HorizonDB is delivering about three times more transactions per second. This gain comes directly from the storage architecture, and now that it’s finished, we can see that self-managed Postgres on the left delivered over 4,200 transactions per second. And HorizonDB had more than 11,000 with much lower latency. Performance was a core design principle for HorizonDB from the beginning, and it’s something that you’ll see directly in your applications. - Okay, so performance is ultra fast, and we know this is built for both enterprises and developers, and we know enterprises love security. So what’s different there? - Yeah, so security of course is foundational. It spans everything from network isolation and identity to data protection. Just like performance, it’s been a core focus from day one. And we’re building on the proven enterprise capabilities of Azure database for Postgres to deliver it out of the box like Entra ID integration, where you can enable identity-based access, so users connect securely without managing passwords or private endpoints to lock down access. So the database is only reachable over your private network with no public exposure and of course encryption at rest where data is automatically encrypted on the disk. All of this is available from day one, so you get strong enterprise ready security without any extra setup. - Okay, and bringing this back to our developers who are watching the built-in AI centric features with HorizonDB now also make it easier to build AI apps. - Yeah, and this is an area that we’re really leaning into. Postgres was already popular and chat with your data use cases accelerated that adoption because it natively supports vectors for similarity search. Our focus is on making intelligent retrieval and generative AI work on a massive scale with high accuracy and efficiency. To do that, we’ve rethought how memory and storage work together in the database so that more IO traffic moves to disk letting you take advantage of much larger storage capacity using Microsoft’s disk accelerated nearest neighbor or DiskANN technology. This quantize a vector-based graph in memory and maps it to a full precision graph stored on disk, which significantly reduces memory requirements while still delivering fast higher quality similarity search across your data. - And speaking of generative AI responses, we’ve also made it easier for the database to work directly with AI models. - Yeah, we have, and this starts with AI model management, which automatically registers a set of models with your instance of HorizonDB. It’s really simple. So here’s my HorizonDB, and I’ll just select the enable managed models then confirm. And what this does behind the scenes is it automatically registers a few AI models for use. Once the provisioning is complete, you can see the AI model management blade in the portal with the GPT, text embedding and re-ranking models listed. And you can also bring your own models where you register them through the database directly. - So now you can use models effectively with Postgres without extra manual provisioning configuration. So, how would you interact with them? - Yeah, this is where AI functions come in. These are a set of SQL functions that you interact directly with AI models. The Azure AI extension in HorizonDB provides functions that you can invoke using SQL to leverage AI models. So let me show you a couple. For example, you can use the generate function to produce a response from a prompt. In this case, I’ll say summarize the following customer reviews in two to three sentences, and then I’ll run it and you can see the response is generated as a SQL result that I could use in my app. Alternatively, the extract function is designed to pull specific entities from unstructured text into a structured form that you can then store and query. You can see here that I’m going to use an array to ask for the main product features along with the customer sentiment from my database. Now, we’ll let that run for a moment and we can see the response of producers for each item in the catalog. - And this is just one scenario that you showed with querying, but I can imagine another case where you might say, use data transformation where the results are written back into the database. - Yeah, absolutely. And when you do that, obviously the retrieval time for those results stored in the database is incredibly fast and you’re saving on your tokens as well. - That’s really great to see. So another challenge that we hear a lot of times is around building and maintaining AI pipeline. So we’re making that easier too. - So this is where AI pipelines in HorizonDB extend AI model management and AI functions even further. Today, most generative AI apps rebuild the same steps, chunking, creating embeddings, backfilling data, often in fragile external pipelines. With AI pipelines, all of this is built directly into Postgres. So you can see here that I’ve created a pipeline using the create pipeline function in the AI extension. This chunks the data in a database in real time as it’s added. It will then create embeddings for those chunks using the embedding model that the AI model management enabled for us. And these are ultimately stored in a table. So I can then go ahead and run this, and then if I count the records in the output table, you can see that it’s increasing. And this is all happening asynchronously in the background so that it’s not blocking or really having any real performance impact on my transactional workload. But the best thing about these pipelines is that they’re durable. And this means that I could pause it. And you can see here that the row count stops increasing, even if the workload continues and I can resume it, think of it as a reliable background task. And if my server fails over, it’s no problem, the AI pipeline fails over as well and it just keeps running. - So all that pipeline complexity then just moves into the database and kind of reduces the complexity and runs reliably. - Exactly, and building on this, we can now also use the rank function to re-rank search results with an AI model. Earlier I talked about how DiskANN improved similarity search, but that’s just the first step. With rank, we can apply a model like Cohere to refine the top results and improve overall relevance. Let me show you, I’ve got two identical queries here. One just uses an index and the other wraps the same query in our rank function to improve the relevance of results. If I run the first query for the headphones with the highest playtime and good calling, you can see the results seem pretty good. And these aren’t bad with a few showing around 40 hours. But when I run the rank query that applies the AI ranking model, the top most results are definitely more relevant to my search. Here, there are headphones with 60 or more hours, and the top ranked model has a hundred. - And this is really powerful. So you basically started out with fast similarity search, then used AI to make the results even more relevant. But why don’t we move beyond the database itself and look at what we’ve done then to help with the building experience of Postgres workloads on HorizonDB? - Sure, and this is one of my favorite additions. We’ve built a new VS code extension that brings familiar Postgres tools into a modern AI powered experience. As someone who spent years working in database tools, this is something that I really wish I’d had. It makes debugging and development a lot more easy. So here we’re in VS code and on the left you can see I’m using the Postgres extension. Now this works with any Postgres, but when you connect to Postgres on Azure, you get even deeper integration. So let’s look at an Azure server, and if I right click, you can see a number of server management features like network configuration, backups, and even start stop built in with a single click. And if I right click on tables, one of the most recent additions is object properties. But now let’s run a query. This is a little slower than we’d expect. So I open the new visual query execution plans to debug. It’s easy to identify the Inefficient query operator, and I can click on that to debug further. But the best part is that Copilot can fix it for me. Here, I’ll just click on the Copilot button in the query plan and it gets to work. The Copilot generates the fix for me, and it’s really that simple. Now I want to test this fix in a non-prod server, and that’s easy to. For that, I’ll show you a first look at the new built-in data cloning. I’ll go back up to the server and right click and I can clone it with all my data. And that just takes a moment to validate my solution. - And that’s a real shift. You got the same familiar Postgres experience, but now with AI and platform capabilities that really improve how you build and run apps. So what’s next? - Yeah, so we’ve covered a lot today, but this is just the start. We’re continuing to push the boundaries of what you can do with Postgres on Azure. So here’s a lot more coming. - So what’s the best way then for everyone watching to get started with Azure HorizonDB? - Yeah, so, you know, you can go try it for yourself. It’s in preview now and it’s easy to get started with everything that I demoed. The VS code extension is available in the VS code marketplace. And of course, to stay current, check out our blog at aka.ms/azurepostgresblog. - Thanks so much for joining us today, Charles, and of course, keep checking back to Microsoft Mechanics for the latest tech updates, and we’ll see you again next time.Exploring Azure OpenAI Assistants and Azure AI Agent Services: Benefits and Opportunities
In the rapidly evolving landscape of artificial intelligence, businesses are increasingly turning to cloud-based solutions to harness the power of AI. Microsoft Azure offers two prominent services in this domain: Azure OpenAI Assistants and Azure AI Agent Services. While both services aim to enhance user experiences and streamline operations, they cater to different needs and use cases. This blog post will delve into the details of each service, their benefits, and the opportunities they present for businesses. Understanding Azure OpenAI Assistants What Are Azure OpenAI Assistants? Azure OpenAI Assistants are designed to leverage the capabilities of OpenAI's models, such as GPT-3 and its successors. These assistants are tailored for applications that require advanced natural language processing (NLP) and understanding, making them ideal for conversational agents, chatbots, and other interactive applications. Key Features Pre-trained Models: Azure OpenAI Assistants utilize pre-trained models from OpenAI, which means they come with a wealth of knowledge and language understanding out of the box. This reduces the time and effort required for training models from scratch. Customizability: While the models are pre-trained, developers can fine-tune them to meet specific business needs. This allows for the creation of personalized experiences that resonate with users. Integration with Azure Ecosystem: Azure OpenAI Assistants seamlessly integrate with other Azure services, such as Azure Functions, Azure Logic Apps, and Azure Cognitive Services. This enables businesses to build comprehensive solutions that leverage multiple Azure capabilities. Benefits of Azure OpenAI Assistants Enhanced User Experience: By utilizing advanced NLP capabilities, Azure OpenAI Assistants can provide more natural and engaging interactions. This leads to improved customer satisfaction and loyalty. Rapid Deployment: The availability of pre-trained models allows businesses to deploy AI solutions quickly. This is particularly beneficial for organizations looking to implement AI without extensive development time. Scalability: Azure's cloud infrastructure ensures that applications built with OpenAI Assistants can scale to meet growing user demands without compromising performance. Understanding Azure AI Agent Services What Are Azure AI Agent Services? Azure AI Agent Services provide a more flexible framework for building AI-driven applications. Unlike Azure OpenAI Assistants, which are limited to OpenAI models, Azure AI Agent Services allow developers to utilize a variety of AI models, including those from other providers or custom-built models. Key Features Model Agnosticism: Developers can choose from a wide range of AI models, enabling them to select the best fit for their specific use case. This flexibility encourages innovation and experimentation. Custom Agent Development: Azure AI Agent Services support the creation of custom agents that can perform a variety of tasks, from simple queries to complex decision-making processes. Integration with Other AI Services: Like OpenAI Assistants, Azure AI Agent Services can integrate with other Azure services, allowing for the creation of sophisticated AI solutions that leverage multiple technologies. Benefits of Azure AI Agent Services Diverse Use Cases: The ability to use any AI model opens a world of possibilities for businesses. Whether it's a specialized model for sentiment analysis or a custom-built model for a niche application, organizations can tailor their solutions to meet specific needs. Enhanced Automation: AI agents can automate repetitive tasks, freeing up human resources for more strategic activities. This leads to increased efficiency and productivity. Cost-Effectiveness: By allowing the use of various models, businesses can choose cost-effective solutions that align with their budget and performance requirements. Opportunities for Businesses Improved Customer Engagement Both Azure OpenAI Assistants and Azure AI Agent Services can significantly enhance customer engagement. By providing personalized and context-aware interactions, businesses can create a more satisfying user experience. For example, a retail company can use an AI assistant to provide tailored product recommendations based on customer preferences and past purchases. Data-Driven Decision Making AI agents can analyze vast amounts of data and provide actionable insights. This capability enables organizations to make informed decisions based on real-time data analysis. For instance, a financial institution can deploy an AI agent to monitor market trends and provide investment recommendations to clients. Streamlined Operations By automating routine tasks, businesses can streamline their operations and reduce operational costs. For example, a customer support team can use AI agents to handle common inquiries, allowing human agents to focus on more complex issues. Innovation and Experimentation The flexibility of Azure AI Agent Services encourages innovation. Developers can experiment with different models and approaches to find the most effective solutions for their specific challenges. This culture of experimentation can lead to breakthroughs in product development and service delivery. Enhanced Analytics and Insights Integrating AI agents with analytics tools can provide businesses with deeper insights into customer behavior and preferences. This data can inform marketing strategies, product development, and customer service improvements. For example, a company can analyze interactions with an AI assistant to identify common customer pain points, allowing them to address these issues proactively. Conclusion In summary, both Azure OpenAI Assistants and Azure AI Agent Services offer unique advantages that can significantly benefit businesses looking to leverage AI technology. Azure OpenAI Assistants provide a robust framework for building conversational agents using advanced OpenAI models, making them ideal for applications that require sophisticated natural language understanding and generation. Their ease of integration, rapid deployment, and enhanced user experience make them a compelling choice for businesses focused on customer engagement. Azure AI Agent Services, on the other hand, offer unparalleled flexibility by allowing developers to utilize a variety of AI models. This model-agnostic approach encourages innovation and experimentation, enabling businesses to tailor solutions to their specific needs. The ability to automate tasks and streamline operations can lead to significant cost savings and increased efficiency. Additional Resources To further explore Azure OpenAI Assistants and Azure AI Agent Services, consider the following resources: Agent Service on Microsoft Learn Docs Watch On-Demand Sessions Streamlining Customer Service with AI-Powered Agents: Building Intelligent Multi-Agent Systems with Azure AI Microsoft learn Develop AI agents on Azure - Training | Microsoft Learn Community and Announcements Tech Community Announcement: Introducing Azure AI Agent Service Bonus Blog Post: Announcing the Public Preview of Azure AI Agent Service AI Agents for Beginners 10 Lesson Course https://aka.ms/ai-agents-beginners
