Yes, Llama 4 can be used commercially under the Llama Community License, which grants a broad, royalty-free right to use, fine-tune and deploy the models in products and services. The main restriction is a scale threshold: if a product or service had more than 700 million monthly active users at the time Llama 4 was released, a separate license must be requested directly from Meta rather than relying on the default terms. The license also prohibits using Llama 4's outputs to train or improve any other large language model outside the Llama family, and it requires that products built on it display a Built with Llama attribution. Fine-tuned derivative models that are redistributed must include Llama as a prefix in their name. Meta's acceptable use policy additionally bans certain use cases such as weapons development and unlawful surveillance regardless of company size. For the vast majority of enterprises, none of these conditions block ordinary commercial deployment. Nanobase AI reviews license obligations like these as part of every open-weight model deployment so legal and engineering teams stay aligned before launch.
Test the license against your actual scenario, not the summary
License summaries are useful for a first pass, but the Llama Community License has enough conditional clauses that the safest approach is checking your specific deployment scenario against the text directly, since edge cases around scale, redistribution and attribution trip up teams that only read a summary. The scenarios below cover the situations enterprise teams most commonly ask about.
| Scenario | Generally permitted? | What to verify |
|---|---|---|
| Internal chatbot, no redistribution | Yes | No attribution or naming requirement applies internally |
| Customer-facing product built on Llama 4 | Yes, with attribution | "Built with Llama" notice typically required |
| Fine-tuned model redistributed externally | Yes, with naming rule | Derivative name must begin with Llama |
| Product exceeding 700M monthly active users | Requires separate agreement | Contact Meta directly before launch |
| Using Llama 4 outputs to train a competing LLM | Not permitted | Applies regardless of company size |
| Deployment in a weapons or surveillance use case | Not permitted | Governed by Meta's acceptable use policy |
Check both the license itself and the separate acceptable use policy, since restrictions on specific use cases live in the second document, not the license text.
Attribution is a low-friction but easily missed requirement
The requirement to display a "Built with Llama" notice is easy to satisfy but also easy to forget once a product has been in development for months before anyone reviews licensing. Building the attribution notice into the standard release checklist, alongside other third-party license disclosures a product typically already tracks, is more reliable than a one-time manual check before launch.
Treat Llama attribution as a standing release-checklist item, not a one-time compliance task to close and forget.
The monthly active user threshold in practice
The 700 million monthly active user threshold sounds distant for most enterprise deployments, but it is worth tracking proactively for any consumer-facing product with real growth ambitions, since crossing it without a separate agreement in place creates a compliance gap that is harder to unwind after the fact than to plan for in advance. Internal enterprise tools essentially never approach this threshold, which is why the vast majority of enterprise Llama 4 deployments never need to think about it again after initial legal review.
For internal and B2B deployments this threshold is rarely relevant, but any consumer product with viral growth potential should track it as a standing item, not a one-time check.
What legal review should actually confirm
- Identify whether the deployment is internal-only or externally distributed, since attribution and naming rules only bind on distribution.
- Confirm current monthly active user count and growth trajectory against the 700 million threshold.
- Check the acceptable use policy against the specific industry and use case, particularly for finance, healthcare or safety-critical applications.
- Document that Llama 4 outputs will not be used to train a separate, non-Llama model.
- Re-run this checklist for each new Llama 4 version, since terms can change between releases even if the previous version's terms stay fixed for already-downloaded weights.
A five-item legal review is enough to clear most Llama 4 deployments; the risk is skipping it, not the license terms themselves.
Frequently asked questions
Does fine-tuning Llama 4 on proprietary data change the license terms?
No, fine-tuning does not change the underlying license. A fine-tuned derivative is still governed by the Llama Community License, including its naming, attribution and output-use restrictions if the derivative is redistributed externally.
Is a separate license required to sell a product built on Llama 4?
Not simply because the product is sold commercially. The default license already permits commercial use. A separate license from Meta is only needed once the monthly active user threshold is crossed, regardless of whether the product is paid or free.
Where should we check for the current, authoritative license text?
Always check the current license text and acceptable use policy published alongside the specific Llama 4 model version on Meta's official channels before finalizing a legal review, since summaries like this one can lag behind wording updates.
How Nanobase AI helps
Nanobase AI reviews license obligations like these as a standard step in every Llama deployment, keeping engineering and legal aligned on attribution, naming and scale-threshold tracking before launch. See our best open-weight LLMs for enterprise guide or the related question on redistributing Llama derivatives. Learn more via our solutions.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.