5 posts
Where does an open source LLM stand in enterprise use? License terms, closed-model comparison, operational load, and in which scenario it makes sense.
Open source LLM comparison: the strengths, licenses, sizes, Turkish performance of Llama, Qwen, Mistral and DeepSeek, plus an enterprise model selection framework.
What is an open-source LLM? An open-source LLM is a large language model whose weights are published openly so you can run it on your own infrastructure. This guide: a clear definition, open weights vs open source, local deployment, licensing, Llama and other models, KVKK/GDPR, comparison with closed models, and FAQs.
Mistral AI's flagship models (Mistral Large 2, Codestral, Mixtral 8x22B MoE, Mistral NeMo, Le Chat), a map of European AI companies (Aleph Alpha, Stability AI, Synthesia, DeepL, Hugging Face, Helsing), the impact of the EU AI Act + GDPR, the open-source and sovereign-AI strategy, and a scenario-based selection guide for Turkish companies.
One of the most common mistakes enterprises make when choosing a large language model is basing the decision only on benchmarks or market hype. In reality, enterprise model selection depends on much more than raw capability: data privacy, licensing, deployment flexibility, customization needs, total cost of ownership, compliance, observability, vendor lock-in, and operational maturity all matter. It also requires a clear distinction between open-source, open-weight, and closed models. This guide provides a structured framework for choosing between open and closed LLM strategies across technical, legal, operational, and strategic dimensions.