# AI Model License Compliance Checklist for Open-Weight Models > Source: https://netray.co/tools/ai-model-license-compliance-checklist > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: Free Tools ## Short answer A practical control checklist for tracking open-weight model license obligations, from commercial use restrictions and attribution requirements to fine-tuned derivative model terms. ## Category on-prem-ai ## Tool **Kind:** checklist **Groups:** #### License identification and inventory **Items:** **Text:** Every deployed model's exact license (Apache 2.0, MIT, Llama Community License, Gemma Terms of Use, or other) is recorded in a central registry **Critical:** true **Text:** The license version and effective date are recorded, since terms have changed between major model releases **Critical:** true Fine-tuned, quantized, and merged model variants are recorded with the license of every base model they derive from Models bundled inside third-party software or platforms are inventoried the same as directly deployed models A named owner is responsible for the license registry staying current as new models are adopted #### Commercial use terms review **Items:** **Text:** For every model with a custom license (not Apache 2.0 or MIT), commercial use permissions are confirmed against your specific use case **Critical:** true Monthly active user thresholds that trigger additional licensing requirements (as in the Llama Community License) are tracked against actual usage Field-of-use or acceptable-use restrictions in the license (such as prohibited military, surveillance, or other flagged use cases) are reviewed against your actual application Any model used to generate output that competes with, or is used to train, another model has that specific restriction checked, since several licenses explicitly prohibit it #### Attribution and redistribution obligations **Items:** **Text:** Required attribution notices (such as prominently displaying a model name per its license) are implemented wherever the license requires them **Critical:** true Redistribution of model weights, whether internally across business units or externally to customers, is checked against the specific license's redistribution terms License and notice files are preserved and passed through unmodified when weights are redistributed internally Any modification disclosure requirements are met when a fine-tuned or modified model is shared beyond the original team #### Fine-tuned and derivative model obligations **Items:** **Text:** It is documented whether a fine-tuned model is legally a derivative work under its base model's license, and what obligations flow through **Critical:** true Naming requirements for derivative models (some licenses require a specific prefix or disclosure that a model is derived from a named base model) are followed License compatibility is checked before merging models with different licenses, since terms do not always compose cleanly Training data used for fine-tuning is checked for its own licensing restrictions, independent of the base model's license #### Ongoing compliance monitoring **Items:** **Text:** License terms are re-reviewed whenever a model is upgraded to a new version, since publishers have changed terms between releases **Critical:** true Monthly active user counts and other usage-based license triggers are monitored on a recurring, not one-time, basis Legal or compliance review is triggered before adopting any model with a non-Apache/MIT license into a new product or customer-facing use case A process exists for responding if a model publisher changes license terms retroactively or discontinues support for a version you depend on **Scoring Note:** AI model license compliance is defensible when at least 90% of all items are complete and every critical item is closed. Unlike most security checklists, the risk here is primarily contractual and reputational rather than technical: a missed commercial use restriction or an unmet attribution requirement can surface during due diligence, an acquisition, or a customer audit, well after the model has been in production for months. ## Content **Intro:** This free AI model license compliance checklist covers the controls needed to track and honor the license obligations attached to open-weight models, and it is written for engineering leads, legal counsel, and procurement teams adopting Llama, Qwen, DeepSeek, Gemma, Mistral, and other open-weight models in production. It spans five domains: license identification, commercial use terms, attribution and redistribution, derivative model obligations, and ongoing monitoring. Open-weight licensing looks simple until you compare them side by side: Apache 2.0 and MIT are genuinely permissive, but several widely used model families ship with custom licenses carrying commercial use thresholds, field-of-use restrictions, and attribution requirements that are easy to miss during a rushed evaluation. **Sections:** #### Why open-weight licensing is not one uniform category **Body:** Treating all open-weight models as interchangeably free to use is the single most common license compliance mistake. Apache 2.0 (used by Qwen, gpt-oss, and many Mistral releases) and MIT (used by DeepSeek's releases) impose almost no restrictions beyond preserving copyright and license notices. The Llama Community License and Gemma Terms of Use are different instruments entirely: both are custom licenses with specific commercial use conditions, including a monthly active user threshold in the Llama license above which a separate license must be requested from Meta, and acceptable use policies that prohibit certain categories of application regardless of scale. **Bullets:** - Apache 2.0 and MIT licenses permit commercial use, modification, and redistribution with minimal restriction beyond notice preservation. - The Llama Community License requires a separate commercial license once monthly active users exceed a defined threshold at the time of deployment. - The Gemma Terms of Use include a specific acceptable use policy that restricts certain application categories independent of scale. - License terms have changed between major model releases from the same publisher, so a review of an earlier version does not cover a later one. #### The controls that matter most **Body:** The critical items center on the two failure modes that actually surface in practice: deploying a model into a use case its license explicitly restricts, and crossing a usage threshold without noticing. Both are genuinely easy to miss, because engineering teams evaluate models for technical fit and rarely have the specific license text in front of them at decision time, and usage thresholds require ongoing monitoring rather than a one-time check at adoption. A central license registry closes the visibility gap that lets both failure modes go unnoticed for months. **Bullets:** - A central license registry recording the exact license and version for every deployed model. - Commercial use confirmation against your specific use case for any model with a non-Apache/MIT license. - Documented attribution wherever the license requires it, since this is the most commonly skipped obligation. - Ongoing monitoring of usage-based license triggers, not just a check performed at initial adoption. #### How to work through the checklist **Body:** Start with inventory, since you cannot assess compliance for a model you have not recorded. Assign legal or compliance review specifically to any model with a custom license before it reaches a customer-facing use case, and treat Apache 2.0 and MIT models with a lighter, faster review path since their restrictions are minimal. Re-review the registry whenever a model is upgraded, since publishers can and have changed license terms between versions, and build usage monitoring for any threshold-based restriction into your regular reporting rather than relying on someone remembering to check. #### How Netray manages model licensing for customers **Body:** Netray evaluates model licensing as a standard part of every model selection decision we make for customers, alongside performance, cost, and security fit. We maintain current knowledge of commercial use terms across the open-weight landscape, flag threshold-based restrictions before they become a problem at scale, and document attribution and redistribution obligations in the deployment architecture we hand off. For customers building products on top of fine-tuned open-weight models, we help clarify derivative work obligations before a legal or customer due diligence process surfaces a gap nobody anticipated. **Cta:** Get a license compliance review of your current model stack before it surfaces in a customer audit or acquisition due diligence. **Faq:** #### Does the Llama Community License restrict commercial use? **Answer:** It permits commercial use, but requires requesting a separate license from Meta once your product or service exceeds a defined monthly active user threshold at the time of deployment, which has changed across Llama releases, so check the specific version you are using. It also includes an acceptable use policy prohibiting certain categories of application. Review both the threshold and the acceptable use policy against your actual deployment before committing to it at scale. #### Are Apache 2.0 and MIT licensed models completely unrestricted? **Answer:** They are close to it. Both permit commercial use, modification, and redistribution with minimal conditions, primarily preserving the copyright and license notice in redistributed copies. Neither imposes usage thresholds or field-of-use restrictions. Models like Qwen, gpt-oss, and DeepSeek's releases under these licenses are the simplest category to adopt from a compliance standpoint, though you should still confirm the specific license file attached to the exact model version you deploy. #### Is a fine-tuned model a derivative work of its base model's license? **Answer:** Generally yes, and most custom model licenses explicitly address this, often requiring the fine-tuned model to be distributed under the same license and sometimes requiring a naming disclosure indicating it derives from the named base model. This obligation typically flows through even multiple rounds of fine-tuning. Document this chain explicitly rather than assuming a fine-tuned model inherits a clean slate simply because you added your own training data. #### What happens if we exceed a usage threshold without requesting the required license? **Answer:** You are technically operating outside the terms of the license, which creates real legal exposure and can become a serious issue during due diligence, an acquisition, or a dispute with the publisher. The fix is straightforward if caught early: monitor usage against known thresholds on a recurring basis and request the required commercial license before crossing it, not after. Catching this proactively is dramatically cheaper than remediating it later. ## Search terms AI model license compliance checklist, Llama license compliance, open model license comparison, Apache 2.0 vs MIT AI license, open-weight model licensing, commercial use AI model license, fine-tuned model license obligations --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json