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Short answer: Meta Llama is best described as an open-weight, source-available model family under custom community licenses, not as an Open Source Initiative (OSI)-compliant open-source AI system. Depending on the release, you may be allowed to download the weights, run them locally, fine-tune them, build products, and redistribute derivatives. Those rights are contractual and conditional, however, and they are not identical across LLaMA, Llama 2, Llama 3, and Llama 4.
The exact license and Acceptable Use Policy for the version you deploy control the result. Commercial-scale services can require a separate license, redistribution carries notice and attribution duties, and Llama 4’s policy withholds rights for its multimodal models from EU-domiciled people and companies whose principal place of business is in the EU.
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Is Meta Llama really open source?
That depends on the definition of “open source.” Meta has used the term publicly. In its July 23, 2024 Llama 3.1 announcement, Meta described the collection as “open source” and highlighted shared model weights, recipes, and safety tools. The Open Source Initiative (OSI), however, applies a broader legal and reproducibility test.
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Llama releases provide valuable artifacts and broad permissions, but their agreements impose conditions such as attribution, acceptable-use rules, a restriction on using Llama materials or outputs to improve another large language model in the Llama 2 agreement, and a special commercial-scale licensing trigger. Those conditions mean “open-weight” or “source-available under a custom license” is the more precise description. Calling a particular release “open source” without identifying the standard being used can mislead a developer about what is legally permitted.
Open-weight and open-source AI are different things
Open weights means that the learned parameters are available to download or otherwise access. With weights, an operator can often run inference on its own hardware, inspect behavior, quantize the model, or fine-tune it. Open weights do not automatically provide the source code, training-data information, or permission to use the model for every purpose.
Open-source AI under the OSI definition is a legal and technical package. The license must grant the four freedoms without requiring permission for each use, and the publisher must provide the preferred-form materials needed to study and modify the system. That can include data documentation, complete training and inference code, and parameters, not only a polished checkpoint.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problems| Question | Open-weight release | OSI-style open-source AI |
|---|---|---|
| Can you obtain learned parameters? | Usually yes; access to weights is the defining feature. | Yes, as part of the preferred form for modification. |
| Can you use it for any purpose without asking? | Only if the specific license says so; custom restrictions are common. | Yes, the four freedoms must be granted permissionlessly. |
| Are training-data details and complete code supplied? | Not necessarily; a release may provide weights and selected code or recipes. | OSI’s definition expects relevant data information and complete relevant training and inference code. |
| Can you redistribute a derivative? | Only within the license’s notices, attribution, policy, and other conditions. | Yes, under terms that preserve the required freedoms. |
Llama is highly usable as open-weight software: developers can perform local inference and adaptation that closed hosted APIs do not permit. That practical openness should not be confused with the separate OSI question of whether every legal freedom and preferred-form artifact is present.
What each Llama release actually changed
“LLaMA” is not one perpetual legal object. The original 2023 release, Llama 2, Llama 3, and Llama 4 have separate agreements and policies. Check the exact files distributed with the checkpoint you intend to use.
| Release | Historical position | What the license structure means |
|---|---|---|
| Original LLaMA (February 24, 2023) | Meta announced a noncommercial license focused on research use cases, with access granted case by case. | Do not treat the original research-focused terms as equivalent to later commercial releases. |
| Llama 2 (2023) | Broader rights for researchers and commercial users under a custom community license. | The agreement grants limited, royalty-free rights to use, reproduce, distribute, copy, create derivative works, and modify Llama Materials, subject to conditions. |
| Llama 3 (2024) | The README describes the models and weights as licensed for researchers and commercial entities. | “Llama Materials” is defined broadly to include model code, trained weights, inference-enabling code, training-enabling code, fine-tuning-enabling code, and related elements. The agreement still includes policy and scale conditions. |
| Llama 4 (2025) | Uses the same basic custom-license structure, with a version-specific geographic rule for multimodal models. | The Acceptable Use Policy says rights for Llama 4 multimodal models are not granted to EU-domiciled individuals or companies whose principal place of business is in the EU. |
The progression matters. A statement that was accurate about the original LLaMA release may be wrong for Llama 3, while a permission available for Llama 3 may be limited by the Llama 4 policy. Record the model name, release, checkpoint, license version, and policy version in your deployment documentation.
What the Llama 2 community license permits
The Llama 2 Community License Agreement describes the grant as “a non-exclusive, worldwide, non-transferable and royalty-free limited license.” Within that grant, a licensee may use, reproduce, distribute, copy, create derivative works, and modify the Llama Materials. Those are substantial rights: they can support local deployment, internal applications, fine-tuned checkpoints, and products that incorporate the model.
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The word limited is important. The rights are bounded by the contract and its incorporated Acceptable Use Policy. The agreement is not a blanket dedication of the model to the public domain, and it does not promise that every use is lawful merely because a weight file can be downloaded.
Redistribution requirements
If you redistribute Llama 2 or a derivative covered by the agreement, you must provide a copy of the agreement and retain the required attribution notice. A product download, model hub entry, container image, or customer package should therefore include the license text and attribution in a location users can access, rather than silently shipping the weights.
Use restrictions
The Llama 2 agreement incorporates Meta’s Acceptable Use Policy. It also says users may not use Llama Materials or their outputs to improve another large language model. That restriction affects workflows in which Llama-generated text is collected as training data for a different large language model. It is distinct from ordinary fine-tuning of the Llama model itself, but you should read the exact agreement for the version and derivative you are using before designing a data pipeline.
Warranty and responsibility
The materials are provided “AS IS.” The agreement places responsibility on the user to determine whether the model and its outputs are appropriate for a particular application. A license grant is therefore not a quality guarantee, a safety certification, or a promise that generated content will meet regulatory or contractual requirements.
How Llama 3 expands the covered materials
The Llama 3 license defines “Llama Materials” broadly. The term covers model code, trained weights, code that enables inference, code that enables training, code that enables fine-tuning, and related elements. This wording matters when you build a derivative: obligations can follow more than the headline checkpoint file.
Llama 3’s broader commercial availability does not turn the agreement into an unrestricted OSI license. The Acceptable Use Policy remains part of the permission structure, and the 700-million-monthly-active-user term continues to matter. Treat the README, community license, and policy as one set of documents, not as optional reading around a free download.
What Llama 4 changes for EU users
Llama 4 retains the custom-license approach but adds an explicit geographic rule for multimodal models. Its Acceptable Use Policy says rights for Llama 4 multimodal models are not granted to an individual domiciled in the European Union or to a company whose principal place of business is in the European Union.
This is narrower than a statement that every Llama 4 capability is unavailable in the EU. The surfaced restriction specifically concerns multimodal models and the covered EU person or company. Do not assume that a text-only checkpoint has the same treatment, and do not assume that a non-EU subsidiary automatically resolves the issue if the contracting entity or principal place of business falls within the policy language. Identify the exact model modality and the legal entity that will receive the license before deployment.
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Can you use Llama commercially?
For later releases, the answer is often yes, but “commercial” is not a single permission that overrides the rest of the agreement. Llama 2 and later agreements provide commercial access subject to their license, Acceptable Use Policy, attribution and redistribution obligations, and the scale clause described below. The original 2023 LLaMA release was expressly research-focused and noncommercial, so a project cannot rely on later Llama 3 or Llama 4 language when it actually uses an original LLaMA checkpoint.
The 700-million-user clause
Llama 2, Llama 3, and Llama 4 agreements contain a commercial-scale term tied to products or services with more than 700 million monthly active users. If the relevant products or services exceeded 700 million monthly active users in the preceding month at the model’s release date, the operator must request a separate license from Meta.
This is a threshold and timing test, not a claim that every company with a large total user base automatically needs permission. Measure the users of the relevant product or service, use the preceding month specified by the agreement, and compare the result with the model’s release date. A service below the threshold does not escape the other license conditions, and a service above it should not deploy on the assumption that the community license alone is sufficient.
What the clause does not mean
- It does not make ordinary commercial use by smaller services noncommercial.
- It does not remove attribution, acceptable-use, or redistribution duties.
- It does not provide a universal 700-million-user allowance that applies identically to every future release.
- It does not answer whether a particular product is covered; the agreement’s definitions and facts about that product control.
Can you fine-tune Llama and sell the result?
Usually, a later Llama license is designed to permit modification and derivative works, which can include fine-tuning. Selling access to a fine-tuned model or an application built around it is not automatically prohibited. The safe answer is conditional: you must satisfy the release-specific agreement and policy, preserve required notices, avoid prohibited uses, and check whether your scale or geography triggers an additional rule.
A practical pre-launch checklist
- Identify the exact artifact. Record whether the checkpoint is original LLaMA, Llama 2, Llama 3, Llama 3.1, Llama 4, or a derivative distributed by another party. Save the license and policy versions that accompanied it.
- Read the grant and definitions. Confirm that the agreement covers the operations you need: inference, fine-tuning, modification, internal use, distribution, and commercial use. For Llama 3 and later, review what “Llama Materials” includes.
- Classify the product. Decide whether you are distributing weights, a fine-tuned checkpoint, an on-premise installer, an API service, or an application that calls a hosted model. Different distribution paths can create different notice obligations.
- Check geography and modality. If Llama 4 is involved, determine whether the model is multimodal and whether the recipient is an EU-domiciled person or an EU-principal-place-of-business company.
- Check scale. Determine whether the relevant product or service exceeded 700 million monthly active users in the preceding month at the model’s release date. If it did, request the separate license before launch.
- Build the notices into the release. Include the community license, required attribution, and any policy notice where customers or downstream developers can find them. Keep those files with containers, model bundles, and source repositories rather than in an internal-only folder.
- Audit data use. Do not use Llama materials or outputs to improve another large language model where the applicable agreement prohibits that practice. Document what data is used for your own fine-tuning and what model receives it.
- Review safety and law separately. Test the model for your domain, apply the Acceptable Use Policy, and obtain legal advice for regulated or high-risk deployments. The license does not certify outputs or compliance.
Redistributing a Llama-based product
Redistribution is where many “download and ship” plans fail. A derivative checkpoint, a software package that includes model files, and a customer appliance can all expose the license to downstream users. For Llama 2, the agreement specifically requires passing along a copy of the agreement and retaining the attribution notice. Llama 3 and Llama 4 should be handled with the same discipline while you verify their current wording.
Keep a bill of materials showing which files are Meta’s materials, which are your code, which are third-party components, and which are newly trained parameters. Put license notices beside the model download and in the product’s legal or about screen. If you expose an API rather than weights, document whether any customer receives a copy of Llama Materials and what terms apply to that access.
Do not imply that Meta endorses your application, that your fine-tuning data is approved, or that the model’s trademark is included in the license. The community license grants rights to the covered materials; it does not automatically grant rights to every brand, dataset, or piece of content associated with them.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why the OSI classification matters to organizations
The label affects procurement, governance, and portability. An organization that requires OSI-compliant open-source software may reject a custom community license even when engineers can download and modify the weights. A team that only needs local inference and fine-tuning may consider Llama practical despite that classification.
Questions for procurement and compliance
- Does policy require permissionless use for any purpose, or is a custom commercial license acceptable?
- Must the supplier provide training-data information and complete training code, or are weights and inference code enough for the project?
- Can the organization redistribute derivatives to customers and partners?
- Are there restrictions on generated data, model improvement, user geography, or product scale?
- Can the organization preserve notices and attribution in every deployment format?
Answer those questions before selecting a checkpoint. “Open source” in a vendor slide does not resolve them; the license text and the OSI definition answer different questions.
Common mistakes and how to avoid them
Treating all Llama names as one license
Do not copy a Llama 2 compliance checklist into a Llama 4 deployment without checking the new policy. Keep version-specific records and pin the exact model identifier in code and documentation.
Assuming downloadable means unrestricted
A public weight file proves availability, not permission for every use. Read the grant, the Acceptable Use Policy, and any scale or geographic clauses before training or shipping.
Calling a fine-tuned checkpoint “your model” without qualification
Your new parameters and code may be your contributions, but a derivative can still contain or depend on Llama Materials. Preserve the applicable notices and explain the model’s lineage to downstream users.
Ignoring the preferred-form issue
Weights can be enough for practical adaptation while still falling short of OSI’s preferred-form requirement. If reproducibility, auditability, or open-source certification matters, ask what data information and complete code are actually available.
Overlooking the EU multimodal rule
For Llama 4, check the user’s domicile or company principal place of business and the model’s modality before granting access. A general statement that “Llama 4 is available worldwide” would be too broad for the policy language.
A decision framework for developers
| Your priority | What to verify | Likely implication |
|---|---|---|
| Run a model privately on your own hardware | Inference rights, acceptable-use rules, and the exact release license. | Open weights may provide the needed technical access, but contractual limits still apply. |
| Fine-tune and sell an application | Modification and commercial rights, derivative notices, policy compliance, and scale. | Later Llama licenses may permit it when all conditions are met; original LLaMA’s research-only terms do not. |
| Redistribute a checkpoint | Copy of the agreement, attribution, downstream notices, and any restrictions on the derivative. | Package compliance files with the model and keep a record of the source version. |
| Operate a very large consumer service | The 700-million-user test using the preceding month at the model’s release date. | Request a separate Meta license if the threshold is met. |
| Deploy Llama 4 multimodal in Europe | Whether the recipient is EU-domiciled or an EU-principal-place-of-business company. | The policy withholds rights for that covered EU person or company; obtain a legal determination before deployment. |
| Need OSI-compliant open-source AI | Permissionless four freedoms plus preferred-form data information, complete relevant code, and parameters. | Do not assume Llama’s marketing label satisfies the OSI standard. |
Bottom line for Llama users
Llama gives developers more control than a closed API: later releases generally allow downloading, running, adapting, and commercializing the model within a custom contract. That makes Llama a powerful open-weight option. It is not the same as an unrestricted OSI-open-source system, because the licenses impose conditions and the release package may not provide every preferred-form artifact OSI expects.
Before using or selling anything built with Llama, identify the release, read its community license and Acceptable Use Policy, preserve redistribution notices, check the 700-million-user trigger, and apply Llama 4’s EU multimodal rule where relevant. Those checks—not the marketing label alone—determine what your project can safely do.
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