The Sovereign AI Stack

A framework for building enterprise AI capabilities using open-source tools, ensuring data sovereignty, cost predictability, and vendor independence.

Executive Summary

Organizations adopting AI face a fundamental choice: rely on proprietary SaaS platforms or build sovereign infrastructure. This white paper presents a practical framework for the latter approach, demonstrating how open-source tools can deliver enterprise-grade AI capabilities while maintaining full control over data and costs.

The Case for Sovereignty

Data Control

Traditional SaaS AI platforms require sending sensitive data to third-party servers. For industries with regulatory requirements or competitive sensitivity, this creates unacceptable risk.

Sovereign approach: Self-hosted models and vector databases keep all data within your infrastructure boundary.

Cost Predictability

Token-based pricing for cloud AI services creates unpredictable costs that scale with usage. A successful AI deployment can become prohibitively expensive.

Sovereign approach: Fixed infrastructure costs with unlimited inference capacity.

Vendor Independence

Proprietary AI platforms can change pricing, terms, or capabilities at any time. Migration costs create lock-in.

Sovereign approach: Open-source components with standard interfaces allow substitution without architectural changes.

Reference Architecture

The EthosPower Sovereign AI Stack consists of five layers:

1. Inference Layer

Component: Ollama + Local LLMs

Run foundation models locally:

• Llama 3 70B for complex reasoning
• Mistral 7B for fast responses
• Code Llama for development tasks

2. Vector Storage Layer

Components: Qdrant + ChromaDB

Store and query embeddings:

• Qdrant for production workloads
• ChromaDB for development/testing
• Hybrid search combining semantic and keyword matching

3. Knowledge Graph Layer

Component: Neo4j

Model relationships and context:

• Entity extraction from documents
• Relationship mapping between concepts
• Graph-augmented retrieval

4. Orchestration Layer

Components: n8n + Task Master AI

Coordinate workflows:

• Visual workflow builder
• Task decomposition and planning
• Human-in-the-loop approvals

5. Interface Layer

Components: AnythingLLM + Custom UI

User-facing applications:

• Chat interfaces
• Document processing
• API endpoints

Implementation Patterns

RAG Pipeline

Document → Chunking → Embedding → Vector Store
                                       ↓
Query → Embedding → Similarity Search → Context Assembly → LLM → Response

Multi-Agent Orchestration

User Request
     ↓
Planner Agent → Decomposes into subtasks
     ↓
Worker Agents → Execute subtasks in parallel
     ↓
Reviewer Agent → Validates and synthesizes
     ↓
Response

MCP Integration

Model Context Protocol enables standardized tool access:

// Any LLM can use the same tool interface
const tools = [
  { name: "erpnext/get_documents", description: "Query ERPNext" },
  { name: "neo4j/read_cypher", description: "Execute graph queries" },
  { name: "qdrant/semantic_search", description: "Search vector store" },
];

Cost Comparison

SaaS Approach (Annual)

Service	Cost
OpenAI API	$24,000
Pinecone	$8,400
Zapier	$7,200
Notion AI	$3,600
Total	$43,200

Sovereign Approach (Annual)

Component	Cost
GPU Server (lease)	$12,000
VPS Hosting	$2,400
Domain/SSL	$200
Total	$14,600

Savings: 66% ($28,600/year)

Break-even typically occurs within 4-6 months.

Getting Started

Phase 1: Foundation (Month 1)

1. Deploy Ollama with base model
2. Set up Qdrant for vector storage
3. Build basic RAG pipeline
4. Create simple chat interface

Phase 2: Enhancement (Month 2-3)

1. Add Neo4j for knowledge graphs
2. Implement multi-agent orchestration
3. Connect MCP tools
4. Build workflow automation

Phase 3: Production (Month 4+)

1. Harden security and access controls
2. Implement monitoring and observability
3. Document and train users
4. Iterate based on feedback

Conclusion

The sovereign AI stack provides a practical path to enterprise AI capabilities without sacrificing control or budget predictability. By leveraging mature open-source tools and standard protocols, organizations can build AI infrastructure that evolves with their needs while maintaining full ownership of their data and intellectual property.