Common workloads and use cases

Common workloads and use cases#

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.

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