Open-Source vs Proprietary LLMs for Business

The first distinction is vocabulary. Most so-called open-source LLMs are more precisely open-weight. The trained parameters can be downloaded, but the training data, and often the full training code, are not released. In 2024 the Open Source Initiative published an Open Source AI Definition that asks for more than weights, and many popular models do not meet it. For a business, what matters is the license text itself. Licenses vary widely. Mistral 7B was released under the permissive Apache 2.0 license, and DeepSeek released R1 under the MIT license. Meta's Llama models use a custom community license. It permits commercial use but requires companies above 700 million monthly active users to get a separate license from Meta, and it includes an acceptable use policy. Google's Gemma models have their own terms. Read each license's restrictions on use cases and attribution, and check whether outputs may be used to train other models. Proprietary models, such as OpenAI's GPT family, Anthropic's Claude and Google's Gemini, are accessed through APIs, consumer products or cloud platforms. They have often led on the hardest benchmarks. They come with enterprise agreements, support and safety tooling, and you have no infrastructure to manage. The tradeoff is less control. The vendor can change prices, update behavior or retire model versions on its own schedule. On data control, self-hosting an open model keeps data in your own environment. Proprietary models are also offered through cloud platforms with regional hosting, and many providers state that business API data is not used for training by default. Check this in the contract rather than assuming it. On cost, open weights avoid per-token fees but add infrastructure and staff costs. Many companies end up with a hybrid: proprietary models for demanding tasks, and open models for high-volume, sensitive or specialized ones.