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Concepts

Open-Source vs Closed-Source AI

Open-source AI models (Llama, Mistral) release their weights publicly for anyone to use, modify, and deploy. Closed-source models (GPT-4, Claude) are only accessible through APIs. Open-source offers control and privacy; closed-source often leads in capability.

Why it matters

This choice shapes cost, privacy, and control. Open models let you download the weights, run them on your own hardware, customize freely, and keep data in-house, which appeals to developers and privacy-conscious teams. Closed models offer polished, powerful performance through an API but keep you dependent on the provider. Knowing the trade-off helps you pick the right approach for your budget, technical skills, and data-sensitivity needs.

In practice

A startup handling sensitive client data runs an open model like Llama on its own servers so nothing leaves the building, even if it means more setup work. A solo creator who just wants top quality with zero maintenance instead pays for a closed model's API. A practical tip: start with a closed API to prototype fast, then consider open models once cost or privacy becomes a real constraint.

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