EnterpriseDB simplifies AI development with low-code tools and data observability for PostgreSQL environments

EnterpriseDB Advances AI Development with Key Platform Update

EnterpriseDB has rolled out new features for its EDB Postgres AI platform that simplify AI application development and management. The update introduces a low-code/no-code interface alongside enhanced data observability across hybrid environments, addressing common challenges enterprises face when moving AI projects from development to production.

Launched initially in May 2024, EDB Postgres AI is a unified database platform designed to handle transactional, analytical, and AI workloads. It supports both relational and nonrelational data, offers automated data pipelines, and provides development tools for applications.

Addressing the AI Production Gap

Many enterprises struggle to deploy AI projects at scale, with failure rates estimated near 80%. EnterpriseDB’s new capabilities aim to reduce these barriers by streamlining the building and management of AI systems. This is particularly important as few organizations have successfully rolled out agentic AI across large Postgres estates.

Why PostgreSQL Matters for AI

EnterpriseDB’s platform is built on PostgreSQL, which is currently the most popular database. PostgreSQL’s flexibility supports analytical and transactional workloads as well as geospatial, time series, JSON, and vector data. This versatility makes it well-suited for incorporating unstructured data into AI applications.

Recent acquisitions of PostgreSQL vendors by Databricks and Snowflake highlight growing industry interest in using PostgreSQL as a foundation for AI development.

Key New Features

• Low-Code/No-Code Environment: A user-friendly interface combined with an SDK enables setting up AI pipelines with as few as five lines of code.
• GPU Integration: Partnership with Nvidia NIM provides GPU acceleration and a microservices architecture to run AI models locally, enhancing processing power and data security.
• Hybrid Data Observability: Monitoring across hundreds of PostgreSQL databases regardless of whether they’re on-premises or in the cloud, with over 200 built-in metrics and automated issue recommendations.
• Data Security Enhancements: Introduction of transparent data encryption to protect sensitive information.
• Optimized Analytics Engine: Support for Apache Iceberg and Delta tables tailored for AI workloads.
• Universal Operational Data Store: Enables application development using diverse data types, including structured and unstructured data.

These updates stem from customer feedback focused on better leveraging existing Postgres data for analysis, security, and integration with generative AI workflows.

Future Focus: Scale, Performance, and Partnerships

With the foundation for AI development established, EnterpriseDB plans to concentrate on scaling and improving performance. Expanding its partner ecosystem beyond current collaborations with companies like Red Hat and Supermicro will be a priority to reach new markets and customer segments.

Additional opportunities include:

• Adding more industry-specific tools.
• Introducing autonomous agentic AI features to automate maintenance tasks.
• Enhancing multi-cloud capabilities for seamless hybrid operations.
• Developing more comprehensive data governance solutions.

Building Visibility

While EnterpriseDB is highly regarded within the PostgreSQL community, its profile remains lower compared to competitors like MongoDB or SingleStore. Increasing visibility could help it compete more effectively with major players dominating the broader database market.

EnterpriseDB, founded in 2004, has raised $67.9 million in funding, positioning it well to capitalize on the growing adoption of PostgreSQL for AI-driven applications.

For IT professionals and developers looking to deepen their AI expertise and explore low-code AI development environments, platforms like EDB Postgres AI represent valuable options to consider.

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