Guide & Getting Started

Begin your journey with EDB Postgres® AI by exploring how to deploy, manage, and build intelligent systems across hybrid environments using Postgres, GenAI workloads, and analytics pipelines.


What do you want to do?

Do you want to run analytical queries on your Postgres data? Check out the EDB Postgres® AI Analytics Accelerator documentation, which covers the Lakehouse engine, Delta Lake, Iceberg, and tiered storage.

Do you want to run GenAI or machine learning models on your data? Explore the EDB Postgres® AI Factory, including tools for automated AI data pipelines, agent workflows, and model orchestration.

Do you want to manage hybrid workloads, self-managed databases, or model endpoints? Visit the EDB Postgres® AI Hybrid Manager, which provides a Kubernetes-native control plane.


Hybrid Manager

Hybrid Manager is the control plane that runs EDB Postgres AI. It provides a unified interface for managing database clusters, AI workloads, and analytics engines in any Kubernetes environment.

Start here


AI Factory

AI Factory is a low-code environment for building GenAI systems, vector-enabled retrieval, and data-connected agents. It runs either standalone or orchestrated through Hybrid Manager.

Learning paths


Analytics & Pipelines

Analytics Accelerator enables analytics directly in Postgres by leveraging a vectorized query engine and columnar formats like Apache Iceberg® and Delta Lake. With separation of storage and compute and tiered tables that automatically manage hot and cold data, you can run analytical and transactional workloads concurrently without degrading performance

Start here

Use cases


AreaStart here
Hybrid ManagerHybrid Manager overview
AI Factory101 Learning Path
AnalyticsLakehouse overview

Once you're set up, explore advanced capabilities:

  • Deploy sovereign AI solutions using Hybrid Manager
  • Serve and monitor GenAI workloads
  • Build tiered data lakes with Delta Lake and Iceberg
  • Integrate vector pipelines directly into your database

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