Open Source vs. Open Weight AI: Is There Actually a Difference?
Llama, DeepSeek, and Qwen are routinely called 'open source,' but most of them aren't open source in the traditional sense. Here's the difference between open source and open weight, and why the distinction matters for businesses.
- 01Open weight means a model's trained parameters are published so you can download and run them; open source traditionally means the full recipe, including code and training data, is available too.
- 02Most popular 'open' AI models are open weight, not fully open source, because training data and full training code are rarely released.
- 03Licenses attached to open-weight models vary widely, and some restrict commercial use or impose conditions at large scale.
- 04For most businesses, the practical question isn't the label, it's what the license allows and whether you can run and modify the model the way you need to.
In casual conversation, any AI model you can download tends to get called "open source." It's a convenient shorthand, but it blurs a distinction that matters a lot once you're deciding what your business can actually do with a model.
TL;DR: Open weight means a model's trained parameters (the "weights") are published so anyone can download and run the model. Open source, in the traditional software sense, means the entire recipe is available: code, training data, and the right to inspect, modify, and redistribute it. Most popular "open" AI models are open weight rather than fully open source, since training data and complete training code are rarely released. Licenses also vary widely, and some restrict commercial use or add conditions at large scale, so the license matters far more in practice than whichever label a model carries.
What open weight actually gives you
When a lab releases a model's weights, you can download them, run the model on your own hardware, and often fine-tune it on your own data. That's a meaningful amount of control, and it's the foundation of the self-hosting advantages covered in our guide to open-weight vs. proprietary AI for business. What you typically don't get is visibility into how the model was built: the training data, the full training pipeline, and the decisions made along the way usually stay private.
What open source traditionally means
In traditional software, open source means the source code is available under a license that lets you inspect it, modify it, and redistribute it. Applied strictly to AI, that would imply access to far more than weights: the training code, the data used to train the model, and enough documentation to reproduce it. Very few widely used AI models meet that fuller standard, which is why many in the field push for the more precise term "open weight" for models that only release the finished parameters.
Why the label matters less than the license
Two models can both be described as "open" and carry very different legal terms. Some use permissive licenses that allow broad commercial use. Others restrict commercial use, require attribution, or impose extra conditions once your product passes a certain user count. Before building a product on any downloadable model, read the actual license rather than relying on how the model is described in headlines, since the license, not the label, determines what you're allowed to do.
How to evaluate an "open" model for your business
- Read the license first. Check whether commercial use is allowed, whether there are scale-based restrictions, and what attribution is required.
- Check what's actually released. Weights alone are enough to run and fine-tune a model, but if you need to audit training data for compliance reasons, you'll need to verify whether that information exists.
- Confirm you can run it where you need to. Hardware requirements for larger models can be substantial, which affects whether self-hosting is realistic for your team.
- Compare against hosted options. Many open-weight models are also available through hosted providers, which can be cheaper at low volume than running your own infrastructure, a tradeoff covered in our pricing explainer.
FAQ
Are Llama, DeepSeek, and Qwen open source? They're generally better described as open weight. Their trained parameters are available, but full training data and complete training pipelines typically aren't, and each ships under its own license terms.
Does open weight mean free to use commercially? Not automatically. It depends entirely on the specific license attached to the model, which can range from very permissive to meaningfully restrictive.
Is an open-weight model safer or more private than a proprietary one? It can offer more control over where your data goes, since you can run it on your own infrastructure, but that doesn't automatically make it safer in other respects. Safety behavior and guardrails depend on how the model was trained and how you deploy it.
Can I fine-tune any open-weight model? Generally yes, since you have the weights, though the license may place conditions on what you can do with a modified version, so it's worth confirming before investing in fine-tuning.
Why do so many people call these models open source anyway? Mostly convenience and marketing. The term is familiar and carries positive associations, even though it's technically imprecise for models that release weights without the rest of the recipe.
Last updated: September 23, 2026. Model licenses and release practices change frequently; always check the current license on the model's official page before building on it.
