EnterpriseDB updates WarehousePG with features that fuel AI

WarehousePG upgrade

Many enterprises are increasing their investments in AI initiatives due to advances in AI technology that started with OpenAI's November 2022 launch of ChatGPT.

From an AI perspective, providing real-time streaming and enhanced observability are significant updates, since they put the resulting package into competition with other leading commercial data warehouse relational database offerings for AI.

Applications such as generative AI (GenAI) chatbots and AI agents simplify data exploration and analysis so workers can make informed decisions. They also help organizations to automate certain tasks and processes, improving overall efficiency.

However, building chatbots and agents can be expensive.

Chatbots and agents depend on large volumes of high-quality data to perform properly, far more than is required to inform traditional analytics tools, such as reports and dashboards. Ingesting, integrating, preparing and monitoring data for AI is therefore more time-consuming than for traditional analytics. It also demands significantly more compute power -- both for storage and running workloads -- than analytics tools.

As a result, enterprises are spending far more to develop AI tools than in building analytics applications. To help customers manage AI-related spending, numerous vendors have made cost control a focal point.

For example, AWS targeted cost control with data management features unveiled during its re:Invent user conference in early December. Similarly, database vendors including Aerospike and Neo4j have added performance improvements aimed at helping customers lower their spending.

EnterpriseDB targets cost control by charging WarehousePG users on a per-core pricing model rather than consumption-based billing.

Per-core pricing charges customers based on the number of CPU cores used for their WarehousePG deployment, which fixes the cost. Consumption-based pricing models fluctuates based on usage, which sometimes far exceeds expectations.

Given that the cost of AI development is a concern for many enterprises, EnterpriseDB's per-core pricing for WarehousePG is significant, according to Kevin Petrie, an analyst at BARC U.S.

"AI adopters will appreciate the per-core pricing as an alternative to consumption-based billing," he said. "BARC research shows that software is the biggest contributor to cost overruns with AI projects, and this mitigates that risk."

In addition to per-core pricing, EnterpriseDB's addition of data streaming capabilities in WarehousePG is valuable, Petrie continued.

Stream processing is the continuous, high-speed movement of data from its source, through a data streaming platform, to applications and is a key feature of agentic AI pipelines, enabling agents to act on real-time information.

"We see huge demand for data streaming given the low latency of modern workloads," Petrie said. "Many of the most popular AI use cases -- such as fraud detection, preventive maintenance and price optimization -- can benefit from EnterpriseDB's support for data streaming."

Olofson similarly highlighted WarehousePG's streaming capabilities.

"If you want to incorporate AI processing into a workflow, and not just use it for end-user query, the streaming support is critical," he said.

Beyond per-core pricing and new data streaming capabilities, EnterpriseDB's WarehousePG update includes the following:

• An AI-ready architecture that includes stream processing, native vector search and storage capabilities and in-database machine learning with Python and MADlib.
• Flexible deployment on any cloud or on premises, across regions.
• Data governance capabilities, including data observability to monitor anomalies and changes that can affect AI outputs.

While predictable pricing and streaming capabilities stand out, the WarehousePG update was driven by conversations with customers, according to Quais Taraki, EnterpriseDB's chief technology officer.

"This update was decisively customer-driven," he said.

In addition, EnterpriseDB market research that showed95% of enterprises plan to unify data and AI over the next three years, and that data sovereignty is a point of emphasis, played a role in this release, Taraki continued.

"These capabilities were prioritized to reflect that reality," he said.

Looking ahead

With the latest WarehousePG update now available, EnterpriseDB will focus on improving the interoperability of the EDB Postgres AI platform in 2026, according to Taraki.

Database platforms provide a broad range of capabilities. But to best serve customers building AI and analytics applications, integrations that enable easy connections with external platforms are valuable.

"In 2026, a key focus will be deepening interoperability across the AI and analytics ecosystem," Taraki said. "That means stronger business continuity and security, tighter integration with open data and AI frameworks, and fewer operational barriers for customers modernizing analytics workloads on Postgres."

EnterpriseDB's emphasis on interoperability is wise, according to Olofson, who noted that more partnerships and integrations help position the vendor's database offerings within broader analytics and AI ecosystems.

"EnterpriseDB's platform is quite competitive on its own, but when businesses look to use it in a complex data management environment, especially one supporting AI, they need a constellation of other technologies as well," he said. "The key for EnterpriseDB is in partnering with technology suppliers and consultants that can cast the [EnterpriseDB PostgreSQL] offerings into a context of business solutions."

Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than 25 years of experience. He covers analytics and data management.