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Before using an AI model in a business, check the license and policies for the exact model version and files you plan to use, then match their terms to what your business will actually do. “Open” or “open-weight” does not by itself mean every commercial use, fine-tune, output, or redistribution is permitted.
Start with the activity you want to permit
“Commercial use” can mean very different things. Running a model privately for employees is not the same activity as hosting it for customers, distributing its weights, or using its outputs to train another model. Write down the intended deployment before interpreting the license.
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| Planned activity | What to verify |
|---|---|
| Run the model internally | Whether the grant covers your organization’s use and whether an acceptable-use policy or other restriction applies. |
| Offer a hosted product or service | Whether serving the model or its outputs is permitted, including any restrictions tied to the application or users. |
| Fine-tune the model | Whether modification is permitted and what terms apply to the fine-tuned weights and any materials you distribute. |
| Use outputs or model materials to train another model | Whether the exact version’s terms address this activity; do not infer permission from the right to use the original model. |
| Redistribute weights, derivatives, or a product containing model materials | Whether redistribution is allowed and what notices, agreement copies, attribution, naming, or downstream conditions must accompany it. |
These distinctions matter in real licenses. OpenAI describes gpt-oss as licensed under Apache 2.0, with use subject to its usage policy. Meta’s Llama 4 license includes additional commercial terms and incorporates an acceptable-use policy. Those examples are not interchangeable templates: read the terms for the model you intend to deploy. OpenAI’s gpt-oss documentation · Meta’s Llama 4 license
A practical license-review workflow
1. Identify the exact model and artifact
Record the model family, version or checkpoint, repository or vendor, and date you obtained it. Keep the files distributed with that release, including license, policy, and notice files. Check which materials the terms cover: weights, code, documentation, inference components, fine-tunes, or bundled components. Do not apply one release’s terms to another; terms can differ among model families and versions, as the Apache Software Foundation’s review illustrates.
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2. Read the grant, restrictions, and incorporated terms together
Find the actual license grant and identify its scope: what uses, modifications, transfers, or sublicenses it permits, and whether commercial activity is included. Then look for restrictions and terms incorporated by reference, such as an acceptable-use policy or a separate commercial agreement. A statement that commercial use is allowed does not cancel conditions elsewhere in the applicable terms. Meta describes Llama licensing as a bespoke commercial license, so do not assume it works like Apache 2.0. Meta’s Llama FAQ · Llama 4 license
3. Test your specific product against policy and eligibility rules
Compare the planned use with every applicable acceptable-use policy. Check whether it restricts particular applications, requires disclosures, or sets geographic or other eligibility conditions. For example, Llama 4 materials contain policy and regional language for certain multimodal materials; verify whether a provision applies to your exact model and deployment rather than generalizing across the Llama family. The relevant terms are published in the Llama 4 license.
4. Treat redistribution as a separate compliance task
If you will ship weights, derivatives, or a product containing model materials, translate the license into release requirements. Check whether you must include the agreement, preserve notices, provide attribution, display a statement, follow model-naming rules, or pass conditions to downstream recipients. Add the applicable requirements to packaging, product documentation, and release procedures before launch. Meta’s Llama 4 agreement is one example with distribution conditions and agreement-copy, “Built with Llama,” and naming provisions; consult the agreement itself for their exact scope.
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Do not assume that rights to run a model settle rights to its outputs or to use those outputs to improve another model. Check the exact version’s terms and related official guidance for each activity. Meta’s FAQ distinguishes Llama 2 and Llama 3 from Llama 3.1 and later on use of model materials or outputs to improve other models, so a conclusion about one generation should not be carried over to another. See the Llama FAQ, Llama 2 license, and Llama 3 license.
6. Check rights and terms beyond the model license
A license review is not a complete rights audit. Check applicable third-party notices, component and dataset terms, trademark rules, and rights relevant to the outputs and markets where you operate. The NTIA material discusses variation in model terms, but it does not establish the provenance or rights status of every model’s training data, component, output, or use in every jurisdiction. Treat those as separate questions when material to your deployment.
7. Keep a review record and resolve important uncertainty
Save the artifact identifier, the license and policy versions reviewed, the review date, your written deployment description, and the resulting compliance checklist. Keep any written permission or legal advice with the record. If a material commercial right or downstream obligation remains unclear, pause the affected activity until you resolve it with the rights holder or qualified counsel.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to compare candidate models
Compare the same questions for each exact release, rather than ranking models by the label “open.” A compact comparison can expose trade-offs that a headline summary misses:
- What commercial activities does the grant cover, and what restrictions or policies qualify it?
- Does it distinguish hosted use from distributing weights, derivatives, or a product containing model materials?
- What terms govern fine-tuning and using outputs or materials to train another model?
- Are there notice, attribution, naming, or downstream conditions?
- Do geographic or entity-based eligibility terms apply?
- Can an incorporated policy or separate commercial agreement change what the license summary suggests?
For instance, the published descriptions of gpt-oss and Llama 4 show why the same checklist may produce different answers: OpenAI describes gpt-oss as Apache 2.0 subject to its usage policy, while Meta’s Llama 4 agreement contains additional terms. The comparison must be made from the gpt-oss documentation, the Llama 4 license, and the precise release materials for any other candidate.
Recheck the terms before launch
Licenses, policies, and model releases can change. The sources cited here were reviewed on October 7, 2026; reopen the official terms for the exact version before deployment and preserve the versions you relied on. This is a practical review process, not a model-specific legal opinion. Where the rights or obligations could materially affect your product, get advice from qualified technology or intellectual-property counsel.
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