Self-Hosted AI for Enterprise: Architecture and Deployment Guide

TL;DR — Self-hosted enterprise AI deploys LLM inference, vector search, and knowledge graphs on your own infrastructure. No data leaves your network. The stack: LLM (Llama 3 or Mistral via vLLM/Ollama), vector database (pgvector or Qdrant), knowledge graph (FalkorDB or Neo4j), RAG pipeline (FastAPI + LangChain), embeddings (BGE or E5), reranking (BGE-Reranker), and source connectors (Nango). Self-host when data sovereignty is required, cost at scale exceeds API costs (break-even: ~75-100 users or 100K+ queries/month), or compliance demands on-premise processing (EU AI Act, GDPR, HIPAA). Hardware: 1-2 GPUs for 70B models, 32-128GB RAM for vector search and knowledge graph. Deploy in 1-2 days (basic) or 2-4 weeks (production-grade with ACLs, citations, and audit logging). All components are open-source.

The question isn't "cloud or on-prem?" — it's "what are we protecting, and what does the organization need?" Enterprise AI architecture should be driven by data sensitivity, compliance requirements, and cost at scale, not by vendor convenience.

Self-hosted AI means every component — LLM inference, vector search, knowledge graph, RAG pipeline — runs on your infrastructure. No data leaves your network. No per-query API costs. No vendor lock-in. Full control over retrieval, generation, and permissions.

Self-Hosted vs Cloud API: When to Choose Each

The Self-Hosted AI Stack

Component Selection

Hardware Requirements

Deployment Architecture

Docker Compose (Small to Medium)

version: "3.9"
services:
  # LLM inference
  ollama:
    image: ollama/ollama:latest
    ports: ["11434:11434"]
    volumes: ["ollama_data:/root/.ollama"]
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]

  # PostgreSQL + pgvector
  postgres:
    image: pgvector/pgvector:pg16
    ports: ["5432:5432"]
    environment:
      POSTGRES_DB: aidb
      POSTGRES_PASSWORD: ${DB_PASSWORD}
    volumes: ["pg_data:/var/lib/postgresql/data"]

  # Knowledge graph (FalkorDB)
  falkordb:
    image: falkordb/falkordb:latest
    ports: ["6379:6379"]
    volumes: ["falkor_data:/data"]

  # RAG API
  rag-api:
    build: .
    ports: ["8000:8000"]
    environment:
      OLLAMA_URL: http://ollama:11434
      DATABASE_URL: postgresql://postgres:${DB_PASSWORD}@postgres/aidb
      FALKORDB_URL: redis://falkordb:6379
    depends_on: [ollama, postgres, falkordb]

  # Monitoring
  prometheus:
    image: prom/prometheus:latest
    ports: ["9090:9090"]
    volumes: ["./prometheus.yml:/etc/prometheus/prometheus.yml"]

volumes:
  ollama_data:
  pg_data:
  falkor_data:

Production Considerations

Cost Comparison: Self-Hosted vs Cloud API

Break-even point: ~200 users or ~50K queries/month. Below that, cloud APIs are cheaper. Above that, self-hosting saves dramatically.

Self-Hosted AI Deployment Checklist

• [ ] Assess data sensitivity: does data need to stay on-premise?
• [ ] Calculate break-even: compare API costs vs infrastructure costs at your scale
• [ ] Choose LLM: Llama 3 70B (quality) or 8B (cost), Mistral as alternative
• [ ] Choose inference engine: vLLM (throughput) or Ollama (simplicity)
• [ ] Choose vector database: pgvector (PostgreSQL) or Qdrant
• [ ] Choose knowledge graph: FalkorDB (lightweight) or Neo4j (feature-rich)
• [ ] Set up Docker Compose for development, Kubernetes for production
• [ ] Configure GPU access for LLM inference (NVIDIA driver + CUDA)
• [ ] Implement hybrid retrieval (BM25 + vector + RRF)
• [ ] Add cross-encoder reranking (BGE-Reranker)
• [ ] Set up source connectors with Nango
• [ ] Implement content hash deduplication in ingestion
• [ ] Configure document-level ACLs in PostgreSQL
• [ ] Implement AI citations with source verification
• [ ] Add hallucination prevention: confidence threshold, faithfulness check
• [ ] Set up semantic answer caching for cost reduction
• [ ] Configure Prometheus + Grafana monitoring
• [ ] Set up audit logging for EU AI Act Article 12 compliance
• [ ] Implement backup and recovery (PostgreSQL PITR + vector snapshots)
• [ ] Configure zero-trust security between services
• [ ] Test with real enterprise queries from different departments
• [ ] Plan model update strategy (blue-green deployment)
• [ ] Consider temporal knowledge graphs for versioned facts
• [ ] Document the architecture for company brain integration
• [ ] Set up alerting: latency p99, error rate, cache hit rate, hallucination rate

FAQ

What is self-hosted enterprise AI?

Self-hosted enterprise AI is AI infrastructure deployed on your own servers or private cloud, where all data processing, LLM inference, vector search, and knowledge graph operations happen within your network. No data leaves your infrastructure. The stack typically includes: an LLM (Llama, Mistral via Ollama or vLLM), a vector database (pgvector, Qdrant), a knowledge graph (FalkorDB, Neo4j), a RAG pipeline (FastAPI + LangChain), and source connectors. Self-hosted AI provides data sovereignty, no per-user costs, full control over retrieval and generation, and compliance with EU AI Act, GDPR, and HIPAA.

Why self-host AI instead of using cloud APIs?

Self-host AI when: (1) data sovereignty is required — sensitive data cannot leave your network, (2) cost at scale — API costs for 500K+ queries/month exceed infrastructure costs, (3) compliance — EU AI Act, GDPR, HIPAA, or SOC 2 require on-premise processing, (4) customization — you need full control over retrieval, reranking, and generation, (5) vendor lock-in avoidance — you don't want dependency on OpenAI, Anthropic, or Google. The break-even point is typically 75-100 users or 100K+ queries/month.

What hardware do you need for self-hosted AI?

For LLM inference: 1-2 GPUs (NVIDIA A100 80GB or L40S) for 70B parameter models, or a single GPU for 8B-13B models. For CPU-only inference: 64-128GB RAM with quantized models (GGUF format). For vector search: CPU with 32-64GB RAM is sufficient for up to 10M vectors. For knowledge graph: 16-32GB RAM. Total infrastructure: $2,000-10,000/month for cloud instances, or $15,000-50,000 one-time for on-premise hardware. A single A100 instance costs ~$3-4/hour on cloud providers.

What is the best open-source AI stack for enterprise?

The recommended open-source enterprise AI stack: LLM inference with vLLM or Ollama (Llama 3 or Mistral), vector database with pgvector (PostgreSQL extension) or Qdrant, knowledge graph with FalkorDB or Neo4j, RAG orchestration with FastAPI + LangChain or LlamaIndex, embeddings with BGE or E5 models, reranking with BGE-Reranker-v2-m3, source connectors with Nango, and monitoring with Prometheus + Grafana. All components are open-source and self-hostable.

How long does it take to deploy self-hosted AI?

A basic self-hosted RAG system can be deployed in 1-2 days using Docker Compose with Ollama, pgvector, and a FastAPI wrapper. A production-grade enterprise deployment with hybrid retrieval, cross-encoder reranking, ACL enforcement, citation verification, audit logging, and multiple source connectors takes 2-4 weeks. The longest phase is typically the ingestion pipeline — connecting to data sources, chunking, embedding, and ACL tagging. Start with a single source (e.g., company wiki) and expand.

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