EnterpriseDB has unveiled a suite of new features for its EDB Postgres AI platform, designed to streamline the development and management of AI applications. Among these enhancements is a low-code/no-code environment that facilitates application development, alongside robust data observability capabilities that span hundreds of databases, both on-premises and in the cloud, addressing the critical issue of data quality.
Originally launched in May 2024, EDB Postgres AI serves as a unified database platform adept at managing transactional, analytical, and AI workloads. It integrates relational and non-relational data, automates data pipelines, and provides a comprehensive set of application development tools. The introduction of these features comes at a time when enterprises face significant challenges in transitioning AI initiatives from development to production, with failure rates for AI projects estimated at around 80%. According to Stephen Catanzano, an analyst at Enterprise Strategy Group, the new capabilities significantly enhance the probability of AI projects reaching production by simplifying the processes involved in building and managing AI development systems.
Catanzano remarked, “The latest EnterpriseDB update is significant for users as it introduces industry-first advancements to the Postgres AI platform that enable secure, agentic, sovereign DevOps across Postgres estates. This update is particularly valuable because few enterprises have successfully deployed agentic AI at scale.”
Headquartered in Wilmington, Delaware, EnterpriseDB specializes in database solutions built on the open-source PostgreSQL format. The company faces competition from other database specialists such as MongoDB and MariaDB, as well as tech giants like AWS, Google, Microsoft, and Oracle, which also offer PostgreSQL options.
New capabilities
In response to the growing interest from enterprises in leveraging proprietary data for application development, EnterpriseDB is creating an environment conducive to AI development. Catanzano notes that the company holds a distinct advantage over other data management vendors venturing into AI development, thanks to its PostgreSQL foundation.
PostgreSQL, now recognized as the most popular database, is increasingly favored for AI development due to its flexibility and versatility. It supports a range of workloads, including analytical, transactional, geospatial, time series, JSON, and vector database workloads, facilitating the discovery of unstructured data. The recent acquisitions of PostgreSQL database vendors by Databricks and Snowflake further underscore the platform’s growing prominence in AI development.
Matt Aslett, an analyst at ISG Software Research, emphasizes the importance of EnterpriseDB’s new capabilities, stating that they enhance the vendor’s standing within the PostgreSQL community, despite its relatively lower profile compared to some competitors. He asserts, “EnterpriseDB is well placed to take advantage of increased adoption of PostgreSQL, as well as widespread interest in agentic AI.”
The latest release introduces a low-code environment for creating AI-driven applications, alongside management and monitoring tools for enterprise PostgreSQL estates across hybrid cloud and on-premises deployments. The low-code/no-code capabilities allow users to establish AI pipelines with just five lines of code, complemented by integration with Nvidia NIM, which provides GPUs for enhanced processing performance and a microservices architecture to ensure data security.
Moreover, the new hybrid management capabilities offer data observability across numerous PostgreSQL databases, regardless of their hosting environment. This feature includes over 200 built-in metrics that are automatically monitored, along with recommendations for addressing any detected issues. Additional enhancements include Transparent Data Encryption for improved data security, a new analytics engine optimized for AI workloads with support for Apache Iceberg and Delta tables, and a universal operational data store that accommodates diverse data types, both structured and unstructured.
According to Jozef de Vries, EnterpriseDB’s chief product engineering officer, these enhancements were driven by customer feedback. “These were answers to the practical limits our customers kept hitting,” he explained. “They wanted to get more value out of the data already in their Postgres systems—analyze it in place, secure it, and use it in GenAI workflows. We added what they needed to do that.”
Looking ahead
With the latest additions, EnterpriseDB has established a solid foundation for AI development, according to de Vries. The next phase will focus on scaling and performance, a challenge that many database vendors, including Aerospike and Oracle, are also addressing. Furthermore, EnterpriseDB intends to expand its AI ecosystem through additional partnerships, which de Vries believes is a prudent strategy.
Catanzano concurs, suggesting that broadening their partner ecosystem beyond current collaborations with Red Hat and Supermicro could open new markets and customer segments. Opportunities for growth also lie in developing industry-specific tools, introducing autonomous agentic AI capabilities within the EnterpriseDB platform for maintenance tasks, enhancing multi-cloud capabilities for a seamless experience, and offering a more comprehensive data governance suite.
Aslett advises that increasing visibility will be crucial for EnterpriseDB’s competitive stance. Despite being well-regarded within the PostgreSQL community, the company could benefit from a higher profile to better compete with industry heavyweights. Founded in 2004, EnterpriseDB has raised .9 million in funding, while newer database vendors like SingleStore and MongoDB have garnered significantly more attention and funding.
