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A hands-on field guide for Linux/Bash users weighing open-source vs proprietary AI: outlines trade-offs in transparency, data governance, cost/performance, customization, and reliability; shows how to run llama.cpp locally, call cloud APIs with curl, and containerize with Podman/Ollama; and provides a practical decision checklist on pilots, governance, avoiding lock-in via abstraction, right-sizing models, tracking TCO, and real-world deployment patterns.