EDB Postgres AI Hybrid Manager#
EDB Postgres AI Hybrid Manager (HM) is the control plane for Postgres, sovereign AI, and analytics workloads across Kubernetes-based infrastructures. It provides a consistent and unified way to provision, operate, and monitor PostgreSQL clusters :whether you’re deploying on-prem, in the cloud, or across hybrid environments.
Hybrid Manager enables end-to-end data workflows from database deployment to AI analytics, all governed within your infrastructure.
Why Hybrid Manager?#
Modern data platforms require more than just database provisioning :they need to support:
Kubernetes-native scalability and orchestration
Resilient, high-availability deployments
AI and GenAI integration
Postgres-native analytics with open formats
Governance, observability, and access control
Hybrid Manager was created to meet these demands. It extends Kubernetes capabilities with built-in Postgres expertise, making it easier to operate stateful workloads without sacrificing the flexibility of containerized infrastructure.
Key capabilities#
Area |
Description |
|---|---|
Database provisioning |
Deploy highly available PostgreSQL or Distributed Postgres clusters using the console, templates, or declarative manifests. |
AI integration |
Run sovereign GenAI pipelines , serve models with KServe, and build AI knowledge bases governed by your infrastructure. |
Analytics |
Offload cold data to Delta Lake or Iceberg , query with Postgres syntax, and enable hybrid transactional/analytical processing. |
Monitoring and observability |
Use the integrated dashboards that leverage Prometheus, Grafana, and Loki to obtain deep visibility into query health, resource usage, and system alerts. |
Security and access management |
Integrate with identity providers, define RBAC for projects and clusters, and manage secure access to your databases and services. |
Database migration |
Migrate schemas and data from external databases (Oracle, self-managed Postgres) to HM-managed clusters. Leverage integrated services for migration and schema assessment, data transfer (snapshot), and continuous replication (streaming) to achieve minimal downtime migrations. |
Supported environments#
Hybrid Manager runs on Kubernetes and supports these common enterprise cloud platforms:
Amazon Elastic Kubernetes Service (EKS)
Google Kubernetes Engine (GKE)
HM also supports on-premises hosting platforms:
Red Hat OpenShift (RHOS)
SUSE Rancher RKE2
It supports both cloud-native and on-premises architectures, allowing you to build sovereign, compliant, and flexible data infrastructures.
Integrated solutions#
Hybrid Manager unlocks core functionality across the EDB Postgres AI ecosystem. It consists of:
AI Factory Architecture on Hybrid Manager – Build and run GenAI workloads using private data.
Analytics Concepts in Hybrid Manager – Enable open-format analytics with Iceberg, Delta Lake, and tiered offloading.
Model Image Migration – Accelerate adoption of Postgres with in-platform migration tools.
Operational Monitoring – Monitor operations using built-in observability stack with Kubernetes and database insights.
Cluster lifecycle – Create templates, perform upgrades, configure HA settings, and restore data.
Learn more#
Get started#
Use Hybrid Manager
- Hybrid Manager architecture
- Supported database types
- Known issues
- Multi-DC
- Core platform and resources
- Database cluster engine
- Incorrect database name displayed for EDB Postgres Distributed (PGD) clusters
- PGD-X cluster creation stuck in the “PGD - Reconcile application user” phase
- Failure to create 3-node PGD cluster when
max_connectionsis non-default - PGD database settings are not duplicated when creating or duplicating a second data group
- AHA Witness node resources are over-provisioned
- HA clusters use
verify-cainstead ofverify-fullfor streaming replication certificate authentication - Second node is too slow to join large HA clusters
- Unable to query Knowledge Bases for self-managed clusters
- Backup and recovery
- Replica cluster creation fails when using volume snapshot recovery across regions
- WAL archiving is slow due to default parallel configuration
- Volume snapshot clusters don’t automatically prune WAL files
- Volume snapshot restoration is limited to the same region
transporter-dbdisaster recovery (DR) process may fail due to WAL gaps
- AI Factory and model management
- Analytics and tiered tables
- HM console and observability
- Tags for active model clusters are not displayed on the model details screen
- Chat model cluster metrics are missing from the Grafana model overview dashboard
- User-created Grafana dashboards do not persist after platform redeployment/upgrade
- HTTP 431 “Request Header Fields Too Large” error when accessing the Estate page
- Migrations
- Supported Postgres distributions