Quick summary: Open-weight language models such as Llama, Qwen, Mistral and DeepSeek now reach quality good enough for most enterprise tasks, and can be downloaded and self-hosted without sending data outside.

A few years ago, to get a high-quality language model an enterprise had little choice but to call a closed vendor's API. That has changed. Open-source models — more precisely, open-weight models — now reach a quality good enough to handle most enterprise tasks, and crucially you can download them, run them yourself and stay fully in control.

The ecosystem is rich. Meta's Llama family stands out for general reasoning and a massive support community. Alibaba's Qwen is strong at multilingual tasks, including many Asian languages. Mistral impresses with high performance in compact models that optimise hardware cost. DeepSeek has emerged with reasoning and coding ability that competes directly with leading closed models.

What these models share is open weights: you are given the model's actual parameters to run on your own machines. As a result no data has to be sent out, you control the model version, and you are unaffected when a vendor changes pricing or discontinues a service.

A common misconception is that "open models are weaker". In reality, for most enterprise needs — summarising documents, answering from internal knowledge, drafting, classifying, extracting information — mid-sized open models perform excellently, especially combined with RAG and domain fine-tuning.

Model choice should be driven by real needs, not leaderboard chasing. A small 7–14 billion parameter model can run on a single GPU and is more than enough for an internal assistant; more complex reasoning tasks may need a larger model and multiple GPUs. Balancing quality, speed and hardware cost is the art of deployment.

DABO harnesses the power of this open ecosystem: selecting and optimising the right model for each problem, running entirely within the customer's infrastructure, so you get top-tier AI quality while retaining full ownership.

Frequently asked questions

Are open-source models weaker than closed ones? For most enterprise needs — summarising, internal knowledge Q&A, drafting — mid-sized open models perform excellently, especially combined with RAG.

Which model should we choose? A 7-14 billion parameter model on a single GPU is usually enough for an internal assistant; only more complex reasoning needs a larger model.

Which model is strong in multiple languages? Alibaba's Qwen is strong at multilingual tasks, including many Asian languages.