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Meta Llama is best described as open-weight and source-available under a custom community license, not as OSI-compliant open-source AI. You can generally download Llama weights, run them, fine-tune them, build products with them, and redistribute permitted derivatives, but those rights are conditional. The exact agreement, Acceptable Use Policy, model version, geography and business scale determine what you may do.
The practical rule is simple: do not treat a downloadable checkpoint as a grant of unrestricted commercial rights. Read the license for the exact Llama release you intend to deploy, preserve its notices, follow its use restrictions and check whether a separate Meta license is required.
The short answer: Llama is open-weight, but “open source” depends on the standard
Meta uses “open source” in its product announcements. The Open Source Initiative (OSI), however, applies a stricter test to AI systems. Under the OSI Open Source AI Definition 1.0, announced on October 28, 2024, users must receive permissionless freedom to use, study, modify and share an AI system for any purpose, together with the preferred form for making modifications. For an AI model, that preferred form includes meaningful information about the training data, complete relevant training and inference code, and the parameters.
Llama makes important artifacts available, especially trained parameters and code that helps people run or adapt the models. Its custom license also imposes conditions, including acceptable-use rules, attribution and a restriction on using Llama materials or their outputs to improve another large language model. Those conditions mean the permission is not unconditional. A useful description is therefore open-weight/source-available under a custom license. Whether a particular release meets the OSI definition is a separate standards question, not something established merely by the ability to download its weights.
What “open-weight” means in practice
Weights are the learned parameters
Model weights are the numerical parameters produced by training. Access to them can let an organization run inference on its own infrastructure, inspect behavior, adapt a model with additional training and integrate it into an application without sending every prompt to a hosted API. That is materially more control than an API-only model provides.
Open weights do not automatically provide the data, training recipe, complete source code or legal permission needed to reproduce the model. They also do not automatically grant rights to the training data, Meta trademarks or unrestricted commercial use. The license attached to the release controls what you may do with the weights and related materials.
What OSI’s definition adds
OSI’s Definition 1.0 describes an open-source AI system as one made available under terms that grant four freedoms: use, study, modify and share. The freedoms must be available without requiring permission for a particular user, field of use or commercial activity. OSI also expects the preferred form for modification, including:
- Information about the data used to derive the model, in enough detail to support meaningful study and reproduction.
- Complete relevant code for training and inference, rather than only a binary or a narrow runtime wrapper.
- The model parameters themselves.
A release can be highly useful, locally runnable and fine-tunable while failing that test because it omits preferred-form materials or places contractual limits on use and redistribution.
What the Llama agreements actually license
Llama 2: broad rights with explicit conditions
The Llama 2 Community License Agreement grants a “non-exclusive, worldwide, non-transferable and royalty-free limited license” to use, reproduce, distribute, copy, create derivative works and modify the Llama Materials. That wording is broad enough for many commercial applications, including products that incorporate a fine-tuned or otherwise adapted Llama model.
It is still a limited contract, not a dedication to the public domain. Redistribution requires passing along a copy of the agreement and retaining the required attribution notice. The license incorporates Meta’s Acceptable Use Policy, and it prohibits using Llama Materials or outputs from them to improve another large language model. The agreement also supplies the materials “AS IS” and puts responsibility on the user to decide whether they are suitable for a particular purpose.
Llama 3: a wider definition of the licensed materials
The Llama 3 license defines Llama Materials broadly. In addition to trained weights, it covers model code, inference-enabling code, training-enabling code, fine-tuning-enabling code and related elements. The same basic obligations remain: the Acceptable Use Policy applies, redistribution carries notice and attribution duties, and the 700-million-monthly-active-user term can require a separate license request.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchThat broader definition matters when you build a derivative. A deployment package may contain more than the checkpoint: scripts, configuration, inference components and fine-tuning code can all fall within the licensed materials. Keep the applicable agreement with the complete package you distribute, not just with a download link to the weights.
Llama 4: the same structure plus a specific EU multimodal rule
Llama 4 uses the same general community-license structure and carries forward the major conditions. Its Acceptable Use Policy adds a version-specific geographic restriction: rights for Llama 4 multimodal models are not granted to individuals domiciled in the European Union or to companies whose principal place of business is in the EU.
This restriction is expressly about multimodal models. Do not generalize it into a statement that every Llama 4 use, or every Llama version, is prohibited for every EU user. Determine whether the artifact is multimodal, identify the contracting user and principal place of business, and read the current Llama 4 agreement and policy before deployment.
Permission scope by version
| Release | What the record establishes | Commercial and modification position | Important conditions |
|---|---|---|---|
| Original LLaMA (February 24, 2023) | Meta announced a research-focused release under a noncommercial license, with access granted case by case. | Do not assume the later Llama 2 permissions apply. The original release was aimed at research use and was not a general commercial grant. | Use the original agreement and access terms for that release; its terms are not interchangeable with Llama 2, 3 or 4. |
| Llama 2 (2023) | Community license covering use, reproduction, distribution, copying, derivative works and modification of Llama Materials. | Commercial use is generally permitted within the contract, subject to its conditions and the scale-triggered license request. | Pass along the agreement, retain attribution, follow the Acceptable Use Policy and do not use Llama materials or outputs to improve another large language model. |
| Llama 3 (2024 release family) | License covers code, trained weights, inference-, training- and fine-tuning-enabling code and related elements. | Commercial entities and researchers can use the materials under the version-specific community license, not under an unrestricted OSI-style grant. | Acceptable-use rules, redistribution notices, the model-improvement restriction and the 700-million-user term continue to matter. |
| Llama 4 (2025 release family) | Same basic community-license approach, with version-specific policy language for multimodal models. | Rights depend on the exact artifact and user. EU-domiciled individuals and EU-principal-place-of-business companies do not receive rights for Llama 4 multimodal models under the cited policy. | Check the current agreement, Acceptable Use Policy, model modality and the commercial-scale clause before use. |
The conditions that can change your answer
Redistribution duties
If you redistribute Llama Materials or a permitted derivative, include a copy of the applicable license agreement and preserve the required attribution notice. This applies to a package you ship to customers, a hosted service in which you provide model files to a partner, and other forms of distribution covered by the agreement. Keep a record of which release and license text accompanied each build so a later update does not silently lose the notice.
Acceptable Use Policy
The license incorporates an Acceptable Use Policy. The policy is a contractual boundary, not merely a suggestion. A technically feasible use can still be disallowed if it falls within a prohibited category. Evaluate the planned application, prompts, fine-tuning data and outputs against the policy before launch and again when the policy or model version changes.
No improvement of another large language model
The Llama 2 agreement states that you may not use Llama Materials or outputs from them to improve another large language model. Treat this as a separate compliance checkpoint from ordinary fine-tuning. Fine-tuning Llama itself is different from using Llama-generated data, evaluations or other materials to train a different large language model; your proposed pipeline should make that distinction explicit.
Non-transferability and attribution
The Llama 2 grant is described as non-transferable. You cannot assume that an agreement obtained by one legal entity automatically covers an affiliate, purchaser, contractor or customer. Establish which entity is using the materials and whether your distribution model is allowed. Retain attribution and the agreement wherever the license requires them.
“AS IS” delivery
The agreements provide the materials “AS IS.” That language does not promise accuracy, availability, fitness for a particular purpose or a particular level of safety. It leaves the deployer responsible for testing, security controls, monitoring, content safeguards and deciding whether the model is appropriate for the use case.
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The Llama 2, Llama 3 and Llama 4 agreements include a commercial-scale term tied to 700 million monthly active users. If the relevant products or services exceeded that number in the preceding month at the model’s release date, the organization must request a separate license from Meta. The figure is a threshold for the agreement’s special licensing condition, not a promise that every business below it is automatically compliant.
Apply the clause to the products or services covered by the contract, not to an informal estimate of a model’s popularity. Document the monthly-active-user measurement, the relevant release date and the legal entity making the deployment. If the threshold is met or its application is uncertain, obtain the separate license before relying on the community grant.
Can you use Llama commercially, fine-tune it and sell a product?
Often yes, for Llama 2 and Llama 3 uses that fit the agreement and Acceptable Use Policy, but “commercial” does not mean “unrestricted.” Llama 4 requires an additional modality and geography check, and any release can impose version-specific requirements. The following scenarios show how to analyze a plan without treating the model name as the license.
| Planned activity | Likely position under the cited community terms | Checks before launch |
|---|---|---|
| Run an unmodified checkpoint on your own servers | Generally allowed for releases whose agreement grants use rights, subject to the policy and any geographic or scale condition. | Confirm the exact version, comply with acceptable-use rules, secure the files and keep the license record. |
| Fine-tune Llama for an internal workflow | Generally within the stated modification and derivative-work rights for Llama 2 and later community releases. | Check training data rights separately, ensure the resulting workflow does not violate the policy and do not use Llama materials or outputs to improve another large language model. |
| Sell an application that calls a hosted Llama deployment | Potentially allowed where the agreement covers the deployment and the service does not trigger a special license requirement. | Determine whether you are redistributing model materials, preserve required notices, review user and geography restrictions and assess the 700-million-user term. |
| Sell the weights or a fine-tuned checkpoint | Possible only if the redistribution and derivative-work terms are satisfied. | Include the agreement and attribution as required, identify all included Llama Materials and verify that your customer receives the rights needed for its own use. |
| Use Llama outputs to train a different large language model | Prohibited by the cited Llama 2 condition and subject to corresponding restrictions in later agreements. | Separate evaluation, synthetic-data and training pipelines so prohibited improvement does not occur. |
| Deploy a Llama 4 multimodal model from an EU company | The cited Llama 4 policy withholds rights for multimodal models from an EU-principal-place-of-business company. | Identify modality and principal place of business, then obtain a current written licensing determination before deployment. |
A practical license-checking procedure
- Identify the exact artifact. Record the release name, model variant, checkpoint, tokenizer, code bundle and date you downloaded it. “LLaMA,” “Llama 2,” “Llama 3” and “Llama 4” are not one legal object.
- Collect the matching documents. Save the community license, Acceptable Use Policy and any model card or distribution notice that accompanied that release. Do not substitute a newer agreement for the one governing an older download without checking the terms.
- Map your activity. Write down whether you will run, modify, fine-tune, host, redistribute weights, distribute a derivative, expose an API or use outputs in another training pipeline. Each activity can engage a different clause.
- Check the permission scope. Confirm that the agreement grants the planned use, reproduction, modification and distribution. Look for non-transferability, field-of-use limitations and any requirement to request additional permission.
- Review the Acceptable Use Policy. Evaluate both the application and the data or prompts used to fine-tune it. Add a policy review to your release checklist so a model update does not bypass it.
- Test the model-improvement restriction. Trace where generated text, scores, labels or synthetic examples go next. If they improve a different large language model, stop and obtain legal guidance or redesign the pipeline.
- Prepare redistribution notices. Put the applicable agreement and attribution in the repository, container, download bundle or customer documentation required by the license. Keep notices intact through model conversion and packaging.
- Measure the scale trigger. Determine whether the relevant product or service exceeded 700 million monthly active users in the preceding month at the model’s release date. Preserve the calculation and request a separate Meta license if the clause applies.
- Check geography and modality. For Llama 4, identify whether the model is multimodal and whether the user is an EU-domiciled individual or an EU-principal-place-of-business company. Treat an uncertain classification as a reason to pause deployment.
- Document the decision. Keep the license version, policy version, entity, model hash, notices, user-count analysis and approval with the release record. This makes later audits and model upgrades substantially easier.
Common mistakes and how to avoid them
Calling every downloadable Llama release “open source”
The label hides the difference between access to weights and OSI’s permissionless-freedom and preferred-form requirements. Describe the release as open-weight or source-available under a custom license unless you have separately established that it meets the OSI definition.
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The releases have related structures but are separate legal documents. The original 2023 LLaMA release was research-focused and noncommercial, while later releases broadened access. Llama 4 adds the explicit EU multimodal restriction described above. Always match advice to the exact version.
Believing fine-tuning removes the original obligations
A derivative checkpoint does not automatically become free of the underlying license. Track which Llama Materials remain in the derivative, carry forward required notices and verify that your distribution method is permitted.
Treating output ownership as a complete answer
The community license’s restrictions concern how materials and outputs may be used, including the prohibition on improving another large language model. Separate questions about output rights, privacy, copyright, safety and the license’s model-improvement clause instead of assuming that one answer resolves all of them.
Ignoring the customer’s position
If you distribute a model or derivative, your customer may need the agreement and attribution to exercise the rights you are relying on. Design the delivery package and contract so those notices are not lost when the customer copies, mirrors or fine-tunes the artifact.
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Meta’s description versus OSI’s test
Meta’s July 23, 2024 Llama 3.1 announcement described the collection as “open source” and emphasized the release of model weights, recipes and safety tools. OSI’s October 28, 2024 Definition 1.0 evaluates something broader: permissionless use, study, modification and sharing, plus the preferred-form information and code needed to modify the system.
These statements can both be accurately reported because they use different benchmarks. Meta is describing broad practical access to useful model artifacts. OSI is applying a formal definition that treats legal restrictions and missing preferred-form materials as decisive. When communicating with engineers, procurement teams or customers, name the benchmark you mean rather than relying on the label alone.
What this means for an organization choosing Llama
- For prototyping: open weights can provide local control and fast experimentation, but record the license before the prototype becomes a customer-facing service.
- For fine-tuning: confirm rights to your training data independently, preserve the Llama notices and audit every downstream use of generated data.
- For a commercial product: check redistribution, acceptable-use, non-transferability, the 700-million-user trigger and any version-specific geographic rule.
- For an EU company considering Llama 4 multimodality: treat the cited policy restriction as a gating issue and obtain a current licensing determination before building the deployment.
- For teams requiring OSI-compliant open source: evaluate whether the release provides the required permissionless freedoms and preferred-form materials; do not substitute Meta’s marketing description for that analysis.
Ultimately, Llama’s value is not in a simple open-versus-closed label. Its community licenses can support substantial commercial development, local inference, fine-tuning and redistribution, but only within contract-defined boundaries. The exact version, artifact, activity, entity, geography and scale determine the permission you actually have.
Frequently Asked Questions
Does downloading Llama give me rights to Meta’s training data or trademarks?
No. Access to weights and related code does not by itself grant rights to the underlying training data, Meta trademarks or any use outside the applicable agreement.
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Is the 700-million-user figure a general prohibition on large companies?
No. It is a contractual trigger in the cited Llama 2, Llama 3 and Llama 4 agreements that can require a separate license when the relevant products or services exceeded 700 million monthly active users in the preceding month at the model’s release date.
Can an EU company use every Llama 4 model?
The cited Llama 4 policy specifically withholds rights for multimodal models from EU-domiciled individuals and companies whose principal place of business is in the EU. The exact model modality and current policy must be checked.
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