title: API vs Local Model Privacy-Performance Paradox

insight: The fundamental tension: API models (Gemini, Claude) offer superior capabilities and speed but expose sensitive IP, customer data, and strategic plans to third parties. Local/deployed models protect privacy but sacrifice performance and require infrastructure investment.

reasoning_chain: Genesis built Deep Think bridge to Gemini API for speed (40-45s responses)

reasoning_chain: All 5-domino products (Radar Audit, Widget, Browser Agents, Memory MCP, Marketplace) depend on AI capabilities

reasoning_chain: Memory MCP will store customer data - sending this to external APIs creates compliance and trust risks

reasoning_chain: Browser Agents handling sensitive SaaS credentials via API = potential data leakage

reasoning_chain: Local models (Ollama, self-hosted LLMs) are 10-100x slower but keep all data in-house

reasoning_chain: Hybrid approach needed: API for training/development, local for production customer data

source_context: Kinan: 'solve the paradox dilemma of use api models and lose potential privacy of ip or dont and perform at a slower pace'


title: Hybrid Model Architecture Decision Path

insight: Rather than binary API vs local choice, deploy tiered architecture: API models for fast iteration/development, local models for production customer data, with clear data classification boundaries.

reasoning_chain: Tier 1 (Public/Fast): Gemini/Claude API for prospecting, initial analysis, non-sensitive workflows

reasoning_chain: Tier 2 (Sensitive): Self-hosted models for customer data processing, credential handling, memory storage

reasoning_chain: Tier 3 (Critical): Air-gapped deployment for proprietary algorithm training, strategic planning

reasoning_chain: Data classification engine at entry point routes requests to appropriate tier

reasoning_chain: Cost tradeoff: API = $0.002/1K tokens vs local = $0.0001/1K tokens but 10x infrastructure cost

source_context: Synthesized from Kinan's paradox question and the AaaS architecture discussions about Ephemeral Compute and BYOS security


title: Memory MCP Data Privacy Exposure

insight: Memory MCP is designed to store customer conversation context and preferences - if this runs via external API, all customer intelligence leaks to third-party LLM providers.

reasoning_chain: Memory MCP purpose: persistent context across sessions for personalized AI

reasoning_chain: Contains: customer pain points, preferences, conversation history, business intelligence

reasoning_chain: External API processing = training data exposure risk + compliance violation (GDPR, CCPA)

reasoning_chain: Competitors could potentially access aggregated insights from prompt leakage

reasoning_chain: This is the critical blocker for full API dependency

source_context: Implied from the paradox discussion - Memory MCP was CTM'd in Session 24 and processes sensitive customer data