EDB unveils EDB Postgres AI

Relational database provider EnterpriseDB (EDB) on Thursday introduced EDB Postgres AI, a new database aimed at transactional, analytical, and AI workloads.

EDB Postgres AI, which was internally named Project Beacon during its development, started its life as a data lakehouse project with support for Delta Live Tables and later evolved into a product that combines EDB’s PostgreSQL software and other components such as data lakehouse analytics into a singular unified offering.  

PostgreSQL, which is an object-oriented relational database, has been gaining popularity because it is an open-source database with many potential providers and can be adapted to multiple workloads, said Matt Aslett, director at ISG’s Ventana Research.

“As a general-purpose database, PostgreSQL is suitable for both transactional and analytic applications,” Aslett explained.

The huge ecosystem of PostgreSQL-based databases that leverage the core technology and skills base makes PostgreSQL impossible to ignore, positioning it as a default standard enterprise-grade open-source database, experts said.

According to data from database knowledge base DB-Engines, PostgreSQL has been steadily rising in popularity and is currently the fourth most popular RDBMS (relational database management system) and fourth most popular database product overall in their rankings.

The constant rise in popularity has forced hyperscalers such as AWS, Google Cloud Platform, and Microsoft Azure to create database services built on PostgreSQL. Examples of these databases are AlloyDB, CitiusDB (PostgreSQL on Azure), Amazon Aurora, and Amazon RDS for PostgreSQL. Other rivals of EDB include YugabyteDB and CockroachDB.

What’s new in EDB Postgres AI?

The components of of EDB Postgres AI, which the company describes as an “intelligent data platform,” include a central management console with AI assistance, EDP Postgres databases, data lakehouse analytics, and AI/ML including vector database support.

The console, according to the company, provides a single pane to all EDB Postgres AI operations and helps manage the database landscape of an enterprise, in turn providing better observability. The console comes with an AI agent that can help enterprises manage on-premises databases.

EDB Postgres AI supports most databases that EDB offers including EDB Postgres, EDB Postgres Advanced Server, EDB Postgres Extended Server, and their respective distributed high availability versions. EDB Postgres Advanced Server also provides Oracle Database compatibility, the company said.

The lakehouse analytics module, according to EDB, brings structured and unstructured data together with the help of Nodes to be analyzed. Nodes support multiple formats, the company said, adding that it has built a custom store to support multiple data formats.

The AI/ML module includes vector support, which effectively gives the platform its capability to build AI-powered applications.

Additionally, Postgres AI comes with support for extensions such as Postgres Enterprise Manager, Barman, Query Advisor, and migration tools, such as the Migration Toolkit and the Migration Portal.

The Migration Portal, according to the company, is among the first EDB tools to include embedded AI via an AI copilot that can assist users in developing migration strategies.

The combination of these components or modules result in Postgres AI’s key capabilities such as rapid analytics, observability, vector support, high availability, and legacy modernization.

Explaining rapid analytics as a capability, EDB said that Postgres AI allows enterprises to spin up analytics clusters on demand.

“With EDB Postgres Lakehouse capabilities, operational data can be stored in a columnar format, optimizing it for fast analytics,” the company said in a statement.

EDB added that its acquisition of Splitgraph, a startup that provides a PostgreSQL-compatible serverless SQL API for building data-driven applications from multiple data sources, last year played a foundational role in building out the analytics capability.

The release of EDB Postgres AI saw the company partner with the likes of Red Hat, Nutanix, and SADA.

EDB’s collaboration with Red Hat will enable enterprises to build AI models on Red Hat OpenShift AI and deliver enterprise-grade, day-two operations with EDB Postgres AI, the company said.

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EDB Postgres AI availability and pricing

EDB Postgres AI, according to Jozef de Vries, the chief engineering officer at EDB, is available as a managed service on AWS, GCP, and Azure.

“The analytics functionality is initially available only on AWS, with the other public clouds to follow soon,” Vries said, adding that Postgres AI can also be self-managed on a public cloud and private cloud environment of the customer’s choice.

EDB prices its EDB Postgres offerings on a per vCPU-hour basis. The company also provides a free tier across all of its database offerings.

The EDB Postgres offering costs $0.0856 per vCPU-hour, the company’s subscription listing showed. Other options, such as EDB Postgres Extended Server, EDB Postgres Advanced Server, and their distributed high availability versions cost $0.1655 per vCPU-hour, $0.2568 per vCPU-hour, $0.3424 per vCPU-hour, and $0.2511 per vCPU-hour respectively.

Why is EDB launching Postgres AI?

EDB Postgres AI, according to Aslett of Ventana Research, is being positioned to help enterprises bring AI capabilities to a variety of workloads regardless of deployment location.

“With EDB Postgres AI, EDB is addressing the requirement for data storage and processing both on-premises and in the cloud. This is important for AI-infused applications, which require high-performance AI inference at the point of interaction,” the research director explained.

Omdia’s chief analyst Bradley Shimmin sees the release of EDB Postgres AI as the repeat of the market branding mania from 1980s.

“It does seem like the 1980s with its ‘Turbo’ market branding mania, as we’re seeing a sudden and very pervasive influx of AI-branded products entering the market,” Shimmin said. Shimmin cited recent examples such as Red Hat Enterprise Linux AI and Oracle Database 23ai.

Shimmin said that he sees the EDB Postgres AI release as a mix of marketing hype and maturation of Postgres offerings.

“The Postgres database was certainly capable of supporting AI workloads before this release, plying vector data as a means of steering large language models away from confabulation and toward fact. What we are seeing from vendors like EDB with these AI branded releases is the vertical integration of functionality geared toward streamlining, simplifying, and accelerating AI-infused use cases,” the analyst explained.

The release of EDB Postgres AI coincides with EDB’s strategic move to a new corporate identity, EDB said.

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