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Before using an AI model in a business, check the license and incorporated policies for the exact model version and artifact you plan to use, then match their terms to what your business will actually do. “Open” or “open-weight” does not by itself establish commercial permission, and permission to use a model commercially does not automatically settle redistribution, fine-tuning, output, or policy questions.
1. Identify the exact model and files
Start with the specific model family, release or checkpoint, source repository or vendor, and the date you obtained it. Save the files distributed with that artifact and identify which terms apply to each: weights, code, documentation, inference components, fine-tunes, and bundled materials may not all be covered in the same way.
Do not assume one release’s terms apply to another. The Apache Software Foundation’s review covers different model families and versions, illustrating why the model name alone is not enough to determine its terms. Broader descriptions of open-weight systems also note variation in use and redistribution terms; see the NTIA material.
2. Describe your intended use precisely
Write down the activities your business plans to undertake. “Use the model” is too vague for a license review: terms may distinguish running it internally from offering it to customers or distributing copies of its materials.
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- Run the model for internal business tasks.
- Offer a hosted service or expose model outputs to customers.
- Modify or fine-tune the model.
- Use model materials or outputs to train or improve another model.
- Distribute weights, fine-tuned weights, other derivatives, or a product containing model materials.
For example, Meta’s Llama 4 license addresses distribution and products containing model materials. OpenAI’s gpt-oss documentation describes Apache 2.0 permission while noting that use remains subject to the gpt-oss usage policy. These are model-specific examples, not rules that apply to every model.
3. Read the grant, restrictions, and incorporated policies
In the actual license, identify what permission is granted and to whom. Check whether the grant covers your intended commercial activity and whether it is limited or subject to restrictions on use, modification, sublicensing, transfer, or other conduct. Then follow references to additional policies, commercial terms, or other documents; a license summary is not a substitute for those terms.
For instance, OpenAI characterizes gpt-oss as Apache 2.0-licensed, subject to its usage policy. Meta describes Llama licensing as a bespoke commercial license, and the Llama 4 agreement incorporates an acceptable-use policy. The Meta Llama FAQ and the applicable Llama 4 license are relevant starting points for that model; do not infer that its conditions match Apache 2.0 or another model’s terms.
4. Check policy, geography, and eligibility conditions
Read each policy incorporated into the license and compare its restrictions with your product and use case. Look for prohibited or restricted applications, required disclosures, and eligibility conditions based on geography or the parties involved. Check the current official text for the precise model and your location: the Llama 4 materials include policy and regional language for certain multimodal materials, but that should not be generalized to other releases or situations.
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If you intend to distribute weights, derivatives, or a product containing model materials, check the terms for practical duties that must be met at release. Depending on the agreement, these may include supplying a copy of the agreement, preserving notices, attributing the model, displaying a statement, using a specified model name, or passing conditions on to downstream recipients.
The Llama 4 license provides a concrete example of agreement-copy, “Built with Llama,” and naming provisions. For any model, turn applicable duties into a release checklist covering packaging, product documentation, and distribution processes rather than treating them as informal notes.
6. Review outputs and model training separately
Do not assume that output use or training another model is governed identically to using the original weights. Check the agreement and associated policies for terms about outputs, using model materials to improve another model, or using outputs for that purpose.
Meta’s Llama FAQ distinguishes Llama 2 and Llama 3 from Llama 3.1 and later regarding use of model materials or outputs to improve other models. Consult the license for the exact version you plan to use; a rule stated for one generation should not be carried over to another.
7. Compare candidates against the same questions
When choosing among models, review each candidate using a consistent checklist. A model that permits your immediate use may still be a poor fit if its redistribution duties, policy restrictions, or eligibility conditions conflict with your planned product.
- Does the commercial grant cover the intended activity?
- Which applications are allowed, restricted, or prohibited?
- Are hosted use, weight redistribution, and distribution of derivatives treated differently?
- What do the terms say about fine-tuning and using outputs to train another model?
- Are attribution, notice, naming, or downstream obligations imposed?
- Are there geographic or entity-based eligibility conditions?
- Can a separate policy or commercial agreement affect the result?
Conditions vary by publisher and version. The Apache Software Foundation review, OpenAI’s gpt-oss documentation, and the Llama 4 license illustrate why candidates should be compared on their actual terms, not just their labels.
8. Check rights questions that a model license does not settle
A license review is not a complete rights audit. Check any applicable third-party notices, component terms, trademark rules, and rights relevant to the training data or outputs and the markets where you plan to operate. The NTIA material does not establish the provenance or rights status of every dataset, component, output, or jurisdiction for a particular model. Treat those as separate questions rather than assuming the model license answers them.
9. Keep a record and resolve material uncertainty
Save the exact license and policy versions you reviewed, the artifact identifier, review date, a description of the planned deployment, and the compliance checklist. Keep written permissions or counsel advice with those records. If a material question about commercial rights or downstream obligations remains unresolved, pause the affected activity until it is addressed.
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Licenses, acceptable-use policies, and model releases can change. Recheck the official terms for the exact artifact before deployment. This review process is general information, not a model-specific legal opinion; seek qualified legal advice when the consequences of an unresolved issue are material.
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