Growing Adoption of EDB Postgres AI
Organizations are increasingly turning to EnterpriseDB’s EDB Postgres AI platform, a trend that the company attributes to a rising demand for enhanced control over the data utilized in artificial intelligence systems. This shift is particularly evident among banks, insurers, retailers, and trading firms that are integrating the platform into their core systems, analytics, and AI workloads. As AI initiatives transition from experimental phases to full-scale operations, the focus on data sovereignty has intensified.
Research conducted by MIT Technology Review Insights, in collaboration with EnterpriseDB, indicates that a commitment to AI and data sovereignty is the most significant predictor of success in AI endeavors. Organizations that prioritize control over their data and infrastructure reportedly achieve five times the return on investment compared to their counterparts.
Nancy Hensley, Chief Product Officer at EDB, emphasized the impact of autonomous AI systems on the underlying data layer. “The agentic era is raising those stakes sharply: As autonomous agents move into production, they act on enterprise data at a speed and concurrency that turns the data layer into the control point for the entire AI strategy—and makes owning and governing it a source of capability, not just protection. It’s why the world’s most demanding enterprises are building that foundation on EDB PG AI,” Hensley remarked.
Korean Migrations
EnterpriseDB highlighted two notable examples from South Korea’s financial sector, where database migrations have been motivated by factors such as cost, licensing, and operational flexibility.
- Industrial Bank of Korea: This lender, which focuses on small and medium-sized businesses, migrated 15 core systems to EDB Postgres AI. The bank noted that its previous proprietary database imposed limitations on customization due to restrictive licensing terms. The new platform enabled the conversion of SQL, procedures, and packages with minimal adjustments. Park Cheol-min, Manager of the IT Operations Division, stated, “We’ve achieved a significant reduction in licensing costs compared to Oracle, and that’s a core win that resonates with leadership on the IT budget side.” He also highlighted the platform’s scalability for future AI services.
- Shinhan EZ Insurance: This digital insurer transitioned its entire core system to the public cloud, rearchitecting its infrastructure on the EDB platform. The legacy database licensing model was deemed incompatible with the flexible scaling required in cloud environments, leading to increased maintenance costs. Jin-sun Kim, Infrastructure Lead of the IT Planning Team, noted, “Migrating our core systems to the public cloud was an enormous challenge in the financial sector. EDB’s professional support structure, backed by a global engineering team, played a decisive role in securing our operational stability.” He emphasized the importance of uninterrupted service in the financial industry and the ability to swiftly support new business requests.
Broader Customer Base
Beyond the financial sector, EnterpriseDB reported that companies such as advertising technology firm MNTN, trading venue Euronext FX, and South Korean retailer Kyobo Book Centre have adopted the platform. These organizations are leveraging the software to diminish reliance on specific vendors, manage extensive data workloads, and gain greater control over compliance and operational expenses.
MNTN employs EDB Postgres AI for ad-tech workloads that necessitate large-scale analytical processing. Euronext FX has implemented the platform across four data centers, while Kyobo Book Centre transitioned from a costly 50 TB cloud-only data warehouse to the EDB solution.
A recurring theme among these deployments is the utilization of a single Postgres-based platform for transactional processing, analytics, and AI-related tasks. EnterpriseDB posits that this trend reflects a broader industry initiative to simplify and reduce the costs associated with maintaining separate systems for live operations and data analysis.
Hensley pointed out that the convergence of AI systems with operational data is becoming increasingly critical. “That convergence is exactly what the agentic era requires. Because AI agents retrieve, analyze, decide, and act against live enterprise data in continuous, high-volume workflows, they amplify the cost and latency penalties of stitching together separate platforms for transactions, analytics, and AI,” she explained.
EnterpriseDB also highlighted recent industry accolades for its data management and contributions to the open-source community, reinforcing its position in the market. The platform is being embraced by organizations seeking enhanced control over data governance, location, and the underlying infrastructure.
Among the cited examples, Euronext FX successfully mitigated vendor risk and regained open-source control across four global data centers, showcasing the platform’s capabilities in meeting the demands of modern enterprises.