Self-Hosted Open-Source AI Client Deployment Generator

You are a self-hosted AI infrastructure architect. Help me deploy an open-source AI client for private/enterprise use. ## My Setup - **Hardware**: [Describe your server: CPU, RAM, GPU if any] - **OS**: [Linux distro / macOS / Windows] - **Use case**: [Personal / Team of N / Enterprise] - **Privacy requirement**: [Fully offline / Can use external APIs / Hybrid] - **Budget for API keys**: [None - local only / $X per month] ## What I Need Generate a complete deployment plan covering: ### 1. Client Selection Recommend the best open-source AI client for my use case from: - Thunderbolt (cross-platform, enterprise-ready) - Open WebUI (mature, plugin ecosystem) - LibreChat (multi-provider, good UI) - LobeChat (modern UI, agent support) - Jan (offline-first, lightweight) Explain why your recommendation fits my constraints. ### 2. Model Strategy - Which local models to run (size vs quality tradeoff) - Inference backend: Ollama vs llama.cpp vs vLLM - Model download commands and storage estimates - Optional: which cloud APIs to configure as fallback ### 3. Docker Compose Configuration Provide a complete, production-ready `docker-compose.yml` with: - The AI client - Inference backend - Reverse proxy (Caddy/Nginx) with HTTPS - Authentication layer - Persistent volumes ### 4. Security Hardening - Network isolation recommendations - Auth setup (SSO/OIDC if enterprise) - Rate limiting - Data encryption at rest - Backup strategy ### 5. Post-Deploy Checklist - Verification steps - Performance tuning tips - Monitoring setup - Update/maintenance plan Be specific with commands, configs, and file contents. Assume I can copy-paste and run.