Concepts and architecture

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Sovereign AI and Data Factory combines physical infrastructure, orchestration software, and lifecycle automation into a single system, preassembled and installed in your data center.

Understanding the system architecture and core roles helps clarify how Postgres workloads and AI pipelines are deployed, managed, and scaled.

Node types and roles

As a pre-requisite, please see configuration details in System Specifications. The system includes several node roles—each with distinct responsibilities and configurations.

Administration node

The administration node provides additional software packages, licensing and support tooling for your Sovereign AI and Data Factory. You need 1 administration node per installation

Hybrid Manager nodes

Three Hybrid Manager nodes form a high-availability control plane. They manage Kubernetes scheduling, cluster lifecycle, observability, and API services.

Functions include:

Managing database cluster deployments
Monitoring 200+ metrics from Postgres, OS, and GPU workloads
Coordinating updates and patches
Serving configuration UIs and APIs

These nodes are not used for running data workloads.

Compute nodes

Compute Nodes are used to run Postgres databases, analytics pipelines, or vector workloads. These nodes support:

EDB Postgres Advanced Server or Extended Server
EDB Postgres Distributed (optional)
Lakehouse formats such as Apache Iceberg
Python-based embedding or transformation pipelines

AI nodes (optional)

AI Nodes are GPU-enabled servers that support large-scale model inference and agentic execution. The system includes two types:

2-GPU Nodes for mid-scale model serving (NVIDIA Ada L40S)
8-GPU Nodes for LLM and agent runtime (NVIDIA HGX with B200 GPUs)

These nodes integrate with KServe to provide on-premises model serving and inference routing.

Orchestration and lifecycle

The system runs a preinstalled Kubernetes cluster managed by Hybrid Manager. Customers do not directly access Kubernetes.

Instead, Hybrid Manager provides a web-based interface to:

Deploy Postgres clusters (with high availability)
Install and monitor vector pipelines or AI workflows
Scale workloads to additional nodes
Monitor performance and resource usage

EDB provides lifecycle services for both software and hardware:

Major and minor version upgrades
Security patches
Hardware firmware and BIOS updates
Automated cluster rebalancing and restart handling

What is "sovereign"?

Sovereign in this context means:

All data and models reside in your physical environment
No external cloud dependencies or data sharing
You own the hardware, and control network access
Fully audit-ready, isolated environment for regulated industries

Sovereign AI and Data Factory is suitable for:

Government or national data infrastructure
Financial services subject to local data regulations
Enterprises with strict cloud restrictions or sensitive IP

Supported workload patterns

Each configuration supports a range of use cases:

Workload	Description	Core Compute	Advanced AI
High availability Postgres	Run 3-node HA clusters	✅	✅
Embeddings	Convert internal data into vector form	❌	✅
Retrieval-augmented generation (RAG)	Combine Postgres + LLMs	❌	✅
Inference	Serve models locally using GPUs	❌	✅
Agentic workflows	Build AI agents that act autonomously	❌	✅

For configuration details, see System Specifications.

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