EDB Postgres AI Hybrid Manager

EDB Postgres AI Hybrid Manager (HM) is the control plane of the EDB Postgres AI Platform—a sovereign AI, analytics, and Postgres management solution that runs anywhere you run Kubernetes. It offers a unified experience for deploying databases, running GenAI workloads, and scaling Postgres-native analytics across cloud, on-premises, or hybrid environments.

It is currently supported on the follow Kubernetes distributions for on-premises: Red Hat OpenShift (RHOS), Rancher RKE2, and for in-cloud: Amazon EKS, and Google GKE.

• One platform, full control
  • What you can do
  • Start here
• Hybrid Manager Release Notes
  • Hybrid Manager 1.3.5 release notes
  • Hybrid Manager 1.3.4 release notes
  • Hybrid Manager 1.3.3 release notes
  • Hybrid Manager 1.3.2 release notes
  • Hybrid Manager 1.3.1 release notes
    • HM console
    • Advanced High Availability and Distributed High Availability (PGD)
    • Backup, recovery & storage
    • Core platform functionality & API
  • Hybrid Manager 1.3 release notes
    • Release Highlights
    • New Features and Enhancements
    • Fixes
  • Hybrid Manager 1.2 release notes
    • Release Highlights
    • Deployment Options
    • New Features & Enhancements
    • Observability, Monitoring & Insights
    • Sustainability: ESG Reporting (Preview)
    • Platform Improvements
    • Resources
• EDB Postgres AI Hybrid Manager
  • Why Hybrid Manager?
  • Key capabilities
  • Supported environments
  • Integrated solutions
  • Learn more
  • Get started
    • Hybrid Manager architecture
    • Supported database types
    • Known issues
    • Supported Postgres distributions
• Installing and configuring Hybrid Manager
  • Overview
  • Phase 1: Planning your architecture
  • Phase 2: Gathering your system requirements
  • Phase 3: Deploying your Kubernetes cluster
  • Phase 4: Preparing your environment
  • Phase 5: Installing Hybrid Manager
  • Phase 6: Exploring your post-installation options
  • Configuring HM
    • Planning your architecture
    • Gathering your system requirements
    • Deploying your Kubernetes cluster
    • Preparing your environment
    • Install Hybrid Manager
    • Exploring your post-installation options
    • Configuring Hybrid Manager
• Using Hybrid Manager
  • Where to start
  • For users
  • For Kubernetes administrators
    • Hybrid Manager Terminology
    • Hybrid Manager console
    • Projects
    • Working with clusters
    • Upgrading database clusters in Hybrid Manager
    • Migrating databases with Hybrid Manager
    • Asset management
    • Backups and restores
    • Hybrid Manager CLI - edbctl
    • Using the API
    • For Kubernetes Admin
• AI Factory in Hybrid Manager
  • Example AI solutions you can build
  • Learning paths
  • Use cases and solutions
  • AI Factory in Hybrid Manager workloads
    • Gen AI workloads
    • Model management and serving
    • Vector Engine
  • Hybrid Manager learn content
  • How AI Factory fits within Hybrid Manager
  • Get started
    • Getting Started
    • Manual
    • How To Builders
    • Setup
    • How To Models
    • Operate & Observe
• Analytics in Hybrid Manager
  • Why Analytics in Hybrid Manager
  • Core Concepts and Capabilities
    • Apache Iceberg®
    • Delta Lake
    • Tiered Tables with PGD
    • Lakehouse Clusters
  • How These Capabilities Work Together
  • Getting Started: Quickstart Checklist
  • Explore Further
    • Analytics Concepts in Hybrid Manager
    • Delta Lake in Hybrid Manager
    • Apache Iceberg® in Hybrid Manager
    • EDB Postgres Lakehouse Clusters on Hybrid Manager
    • Tiered Tables in Hybrid Manager
• Sovereign Data and AI Factory
  • Why choose a hardware-based system?
  • What’s inside
  • Supported workloads
  • Hardware configurations
  • Explore capabilities
    • Concepts and architecture
    • Frequently asked questions
    • Glossary and key terms
    • System specifications
    • Common workloads and use cases
    • What to expect