Local-First AI: Privacy Without Compromise

Why we built for local-first. Running AI locally, keeping data private, and still delivering enterprise features.

Cloud AI is convenient. But your data leaves your control.

Local-first AI keeps everything on your machines while delivering the same power.

Why Local-First Matters

Data Sovereignty

Your data never leaves your infrastructure:

• No third-party access
• No data residency concerns
• Full control over retention

Compliance Ready

Meeting regulatory requirements:

• GDPR: Data stays in jurisdiction
• HIPAA: No external processing
• SOC2: Complete audit trail

Cost Predictable

No per-token pricing surprises:

• Fixed infrastructure costs
• No usage-based billing
• Scales with your hardware

The Architecture

Local-First Stack
├── Local Models (Ollama/vLLM)
│   ├── Llama 3
│   ├── Mistral
│   └── Custom fine-tunes
├── Local Embeddings
│   ├── sentence-transformers
│   └── nomic-embed
├── Local Vector Store
│   ├── ChromaDB
│   └── Qdrant
└── Local Orchestration
    ├── MCP servers
    └── n8n workflows

Model Options

Hardware Requirements

Minimum (8B models):

• 16GB RAM
• Modern CPU
• Optional: 8GB VRAM GPU

Recommended (70B models):

• 64GB RAM
• NVIDIA GPU 24GB+
• NVMe storage

Implementation

Ollama Setup

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Pull models
ollama pull llama3
ollama pull nomic-embed-text

# Start serving
ollama serve

API Compatibility

// OpenAI-compatible API
const response = await fetch('http://localhost:11434/v1/chat/completions', {
  method: 'POST',
  headers: { 'Content-Type': 'application/json' },
  body: JSON.stringify({
    model: 'llama3',
    messages: [{ role: 'user', content: 'Hello' }]
  })
});

Hybrid Approach

Local for sensitive data, cloud for non-sensitive:

async function smartRoute(prompt: string, data: Data) {
  if (data.classification === 'sensitive') {
    return localModel.complete(prompt);
  } else {
    return cloudModel.complete(prompt);
  }
}

Performance Comparison

Best Practices

1. Cache aggressively - Local models are stateless
2. Batch requests - GPU utilization matters
3. Use appropriate model sizes - Bigger isn’t always better
4. Monitor resources - Memory limits are real
5. Hybrid when needed - Not everything needs to be local

Local-first is core to our architecture. See Building 40+ Platforms for the full infrastructure.