Generative AI is a once-in-a-generation technological evolution, where natural language is used as an input for artificial intelligence models and used to generate original content in the form of text, images, code, and more.
Supercharging creativity and innovation, generative AI has transformed “knowledge work” like sales and support, improved the employee experience, and reinvented how new products are designed and brought to market. By reducing the time, energy, and cost to create original content, generative AI is pushing the limits on what is possible.
However, to unlock the tremendous potential this new technology can offer, having the right foundation in place to power AI applications is critical. Generative AI is only as good as the data that sits underneath it—nothing is more foundational than the database undergirding it with mountains of data.
The Best Tools for the Job
Knowing your database has the tools to handle all of your data is important, but the speed and efficiency in which it can process it is equally important. No matter how fully featured your database is, if it’s slow and expensive to run, it’s not going to do the trick.
The partnership between Microsoft and AMD means that developers building on Azure Database for PostgreSQL have it all, and are able to power their performance while driving down costs. In fact, 4th generation AMD EPYC™ processors, similar to those offered on Microsoft Azure, show up to 58 percent improved PostgreSQL performance over comparable infrastructure with double the cores, meaning developers can do more work with PostgreSQL for less.
The Hardware Powering the Future
When BMW was looking to increase the speed of its design and prototyping, it turned to Azure Database for PostgreSQL to help it continue to innovate.
“BMW is a master of integration,” says Sebastian Heinz, mobile data recorder co-creator at BMW Group. “Our focus is to create a car that really fits the customer. That requires integration of thousands of digital components.”
Data Hits the Road
Heinz and Christof Gebhart, mobile data recorder co-creator at BMW Group, dreamed of adapting each development car to automatically transmit data over a cellular connection for faster aggregation and analysis. The company created a mobile data recorder (MDR) solution, placing an IoT device in each development car to transmit data over a cellular connection to an Azure cloud platform, where Azure AI solutions facilitate efficient data analysis.
The MDR system and copilot fueled by Azure significantly enhances data accessibility, speeds prototypes and troubleshooting, and elevates development quality. Copilot user interaction logging, conversations, and feedback and the general MDR knowledge base are reliably stored in Azure Database for PostgreSQL to facilitate the machine learning that builds the copilot’s efficiency over time. Azure Database for PostgreSQL also stores vectors, including KQL shots and the copilot’s Retrieval-Augmented Generation (RAG) chat patterns.
The many ways AI can help businesses innovate are almost too numerous to count. As the technology advances, AI is quickly becoming a competitive differentiator for businesses. And with its unique, specific demands, developers know that they’ll need to build future applications with it in mind. When debating key elements such as the scalability, security, and cost of running these groundbreaking AI models it only makes sense for developers to build those apps on the platform best set up to empower them.
As Feddersen says, “Throughout 2024 and into 2025, the Postgres team at Microsoft has continued to innovate across the entire feature spectrum. From enterprise security and improved high availability to autonomous workload optimization and AI features for building new apps, Azure Database for PostgreSQL is ready to run the next generation of AI apps for the enterprise.”
Building Intelligent Apps Intelligently
The MDR system and copilot fueled by Azure significantly enhances data accessibility, speeds prototypes and troubleshooting, and elevates development quality.
The pace of AI is continuing to accelerate,
and proof-of-concepts apps designed to validate AI functionality are now being pushed into production at scale using Azure Database for PostgreSQL,”
The MDR system and copilot fueled by Azure significantly enhances data accessibility, speeds prototypes and troubleshooting, and elevates development quality.
In-database embedding generation enables AI models to understand relationships and similarities between data, making them key to generative AI workloads. These help customers reduce embedding creation time to single-digit millisecond latency, leverage embedding models at a predictable cost and keep data compliant for highly confidential or private workloads.
Microsoft Copilot provides contextual AI-powered support Azure databases aligned with existing customers permissions and able to assist with more than 40+ capabilities.
Azure AI extension enables users to easily integrate artificial intelligence (AI) capabilities into their applications and workflows. The extension provides a unified and consistent interface for accessing various Azure AI services, such as Azure OpenAI, Azure AI Language Services and Azure Machine Learning.
Charles Feddersen, Postgres Partner Director of Product Management at Microsoft
Azure Database for PostgreSQL is ready to run the next generation of AI apps for the enterprise.”
Charles Feddersen, Postgres Partner Director of Product Management at Microsoft
Those building our AI-powered future applications have many choices when it comes to the databases they work with, but according to the 2024 Stack Overflow Developer Survey, PostgreSQL, an open source database, is the top pick for developers.
In the past, most workloads were managed on PostgreSQL databases stored locally. But the demands of intelligent apps increasingly require the scalability, security, and built-in intelligence that only the cloud can provide.
Enter Azure Database for PostgreSQL. On Microsoft’s Azure cloud platform, Postgres is evolving from a developer darling to a top choice for building the next generation of generative and AI-powered applications. The Azure ecosystem is the ideal place to develop apps that utilize the latest advancements in AI with the robust database security and scalability needed to bring them to market at scale. With Azure Database for PostgreSQL, developers can leverage the features critical to the accuracy, efficiency, and performance of these applications.
“The pace of AI is continuing to accelerate, and proof-of-concepts apps designed to validate AI functionality are now being pushed into production at scale using Azure Database for PostgreSQL,” says Charles Feddersen, Postgres partner director of product management at Microsoft.
For example, the latest release of DiskANN—a unique vector indexing algorithm developed by Microsoft Research—empowers developers to build highly accurate, performant, and scalable generative AI applications. Now available directly in Azure Database for PostgreSQL, the new algorithm features optimized storage that allows it to scale beyond the limits of RAM without sacrificing search speed. It uses quantization to store the compressed vectors in a graph in memory and then looks up full vectors from SSD for final comparison retaining precision. By leveraging SSDs to use less memory, it provides unparalleled scalability and efficiency.
Microsoft has also recently announced the release of the Semantic Ranking Solution Accelerator for Azure Database for PostgreSQL, which gives a powerful boost to the accuracy of generative AI apps’ information retrieval pipeline by reranking vector search results with semantic ranking models. And for even greater accuracy, the GraphRAG Solution Accelerator for Azure Database leverages graph-like relationships in the legal data to hone the specificity of the information retrieval pipeline. Additionally, it simplifies the application architecture by using PostgreSQL as both a relational and graph database.
There are always new and innovative Azure features—and our development process benefits strongly from that.”
Sebastian Heinz, Mobile Data Recorder Co-Creator at BMW Group
Because automotive innovation, especially in the digital domain, constantly accelerates, Heinz appreciated that Azure solutions do, too. He noted, “There are always new and innovative Azure features—and our development process benefits strongly from that.” Gebhart agreed, adding, “BMW has always blended luxury and performance with cutting-edge technologies. With Microsoft’s cloud and AI leadership, we could capture the potential of our MDR system to accelerate development of our innovative cars. The MDR, powered by Microsoft Azure, ensures BMW reliability and quality long before the cars hit the road.”
Azure AI extension enables users to easily integrate artificial intelligence (AI) capabilities into their applications and workflows. The extension provides a unified and consistent interface for accessing various Azure AI services, such as Azure OpenAI, Azure AI Language Services and Azure Machine Learning.
improved PostgreSQL performance compared to infrastructure with Double the cores
To help developers find the best options for PostgreSQL on Azure, Microsoft and AMD have worked together to provide a comprehensive migration guide.
All of that efficient performance, of course, is on top of the rich set of features offered on Azure. When the database solutions and hardware features are combined and put to full use, the results are truly game-changing.
Azure Database for PostgreSQL powered by AMD EPYC™ processors combines the stability, breadth, and knowledge base of a legendary piece of open source software with Microsoft’s fully managed, scalable cloud. It allows developers to dive right into making intelligent apps, fueling the next generation of software development.
With Azure Database for PostgreSQL, developers are free to focus on the future: building apps that will forever change the way we live our lives.
Semantic Ranking gives a powerful boost to the accuracy of generative AI apps’ information retrieval pipeline.
DiskANN empowers developers to build highly accurate, performant, and scalable generative AI applications.
GraphRAG leverages graph-like relationships to hone the specificity of information.
Operational testing of those functionalities is performed using a fleet of 3,500 development cars. Before 2018, operational data from the cars was captured using onboard hard drives manually transferred to on-premises servers. Engineers had to wait at least a day to get fresh data for analysis, which slowed design and prototype times in an industry that thrives on innovation.
Our focus is to create a car that really fits the customer. That requires integration of thousands of digital components.”
Sebastian Heinz, mobile data recorder co-creator at BMW Group
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