Common workloads and use cases

Sovereign AI and Data Factory is purpose-built for organizations that require strict control over data, infrastructure, and AI execution. It supports a wide range of workloads while remaining locally managed.

This section outlines why customers choose the system over cloud-based platforms, and which use cases it’s optimized for.

Why use the engineered system over cloud?

Many modern workloads can run in cloud environments—but not all organizations can or should rely on them. Sovereign AI and Data Factory exists to meet the needs of:

1. Data sovereignty and compliance

• All data, models, and workloads remain inside your physical infrastructure.
• No external APIs or third-party storage required.
• Enables deployment in financial, government, healthcare, and national security sectors.

Regulated environments

• Operates with limited internet dependency.
• Supports deployment lifecycles.
• Enables LLM and AI use cases in environments previously closed to them.

3. Unified platform for Postgres + AI

• Combines HA Postgres, vector search, and GPU inference in one system, controlled by your organization.
• Simplifies orchestration, monitoring, and support.
• Eliminates fragmented solutions and duplicate infrastructure.

4. Predictable performance and cost

• No egress fees, throttling, or unpredictable GPU queues.
• Optimized I/O and compute across known hardware.
• Total cost of ownership is clear and controlled.

5. Lifecycle-managed infrastructure

• Joint EDB + Supermicro support agreements.
• Hardware and software patches integrated and coordinated.
• No reliance on customer-led infrastructure management or DevOps.

Supported workload patterns

The following workloads are supported end-to-end on the Sovereign AI and Data Factory system:

High-availability Postgres

• Run mission-critical systems of record with automatic failover.
• Observability and lifecycle baked in.

Hybrid DBaaS

• Serve internal teams with multiple clusters, from one interface.
• Role-based access control and cluster-level visibility.

Embedding and vector pipelines

• Transform data into embeddings using local models.
• Store and query with pgvector in Postgres.
• No API keys or cloud endpoints required.

Retrieval-augmented generation (RAG)

• Query structured internal data and pass it to an LLM.
• Host both retrieval and inference within your own boundary.

On-premises inference

• Serve generative and embedding models on dedicated GPU hardware.
• Use standard containers (e.g., NVIDIA NIM) to abstract model serving.

Agentic AI workflows

• Build AI agents that execute multi-step tasks using internal tools and data.
• No external APIs or data leakage.
• Deploy interfaces in Slack, custom dashboards, or internal tools.