Open-Weight Model Licenses: What to Check Before You Ship

Do enterprise workloads always need the most capable AI model available? More companies are questioning this assumption as the costs of frontier models increase, and many routine tasks do not require more advanced capabilities. 

Due to this, open-weight models are moving beyond developer experimentation and becoming a part of production AI workloads. But deploying open-weight models raises legal and governance concerns. 

Licensing approaches vary from permissive licenses to custom commercial terms. There are restrictions that can create compliance risks or limit deployment options.

So, which license terms should enterprises check before choosing an open-weight model for production? In this article, we explore how open-weight model licenses are diverging in 2026, five key licensing checks, and the risks enterprises should consider before deployment.

Open Weights Don’t Mean Unrestricted Use

A model’s weights are the numerical parameters learned during training that determine its responses. Open-weight models make these weights available for download. This does not include the training code or data. And downloading these weights does not automatically grant rights to modify or commercially deploy the model.

Open-weight models are different from open-source software as they can be subject to custom licensing terms that govern how developers can use and deploy them. These licenses specify the permissions and conditions for commercial use, fine-tuning, redistribution, and hosted inference.

Open-weight models can also require integration with software platforms to run and manage them. Their licenses may impose additional conditions related to intellectual property (IP), attribution, and downstream deployment.

How Open-Weight Model Licenses Are Diverging in 2026

Most major new open-weight models now use permissive licenses, such as MIT or Apache 2.0 licenses. More restrictive terms are concentrated among a smaller group of vendors and older models that remain widely deployed.

This shift has followed several major model releases. OpenAI’s gpt-oss, Qwen3, Mistral 3, Gemma 4, DeepSeek V4, and Meta’s Muse Glimmer use either the Apache 2.0 or MIT license. Some recent releases, including Gemma 4 and Muse Glimmer, have just switched to the Apache 2.0 license, reflecting a move toward standardized, permissive licensing.

Some Chinese AI labs use custom licenses that include additional commercial conditions. For example, Moonshot AI’s Kimi K3 requires certain large model-hosting companies to sign a separate agreement. Similarly, Z.ai’s GLM-5.3 mandates that qualifying businesses undergo a security review before using the model commercially.

Licenses vary across model families, which include related models released by the same developer in different versions or sizes. Qwen3 uses the Apache 2.0 license, whereas older Qwen2.5 releases retain a custom license agreement that includes a monthly active user threshold for commercial products and services.

Some licenses also impose conditions on hosted services. Kimi K3’s license requires a separate agreement when a Model as a Service (MaaS) business exceeds $20 million in aggregate revenue over any consecutive 12-month period. GLM-5.3’s license requires a security review when a MaaS operator’s aggregate revenue exceeds $10 billion over the same period.

Commercial and usage restrictions vary by model. For instance, MiniMax M3 has revenue-based conditions for commercial use and specifies prohibited use cases. Licensing terms vary from basic attribution or notice requirements to more conditions on commercial use and deployment.

The table below shows how many recent major open-weight models use permissive licenses, while others retain custom terms with additional commercial restrictions:

Model	License	License family	Release date	Commercial use	Key restrictions
Qwen2.5-72B	Qwen License Agreement	Custom / scale-based	Sep 2024	Conditional	Authorization above 100M monthly active users (MAU); derived-model attribution
Qwen3	Apache 2.0	Permissive	Apr 2025	Yes	Copyright and license notices
gpt-oss	Apache 2.0 + usage policy	Permissive	Aug 2025	Yes	License notices; compliance with applicable law
Mistral 3	Apache 2.0	Permissive	Dec 2025	Yes	Copyright and license notices
Gemma 4	Apache 2.0	Permissive	Apr 2026	Yes	Copyright and license notices
DeepSeek V4	MIT	Permissive	Apr 2026	Yes	Copyright and license notices
MiniMax M3	MiniMax Community License	Custom / scale-based	Jun 2026	Conditional	Attribution and notification; authorization above $20M annual revenue; prohibited uses
Kimi K3	Kimi K3 License	Custom / scale-based	Jul 2026	Conditional	MaaS agreement above $20M annual revenue; display rules above 100M MAU or $20M monthly revenue
Muse Glimmer	Apache 2.0	Permissive	Aug 2026	Yes	Copyright and license notices
GLM-5.3	GLM-5.3 License	Custom / scale-based	2026	Conditional	MaaS security review above $10B annual revenue

Disclaimer: This article is for informational purposes only and does not constitute legal advice. Please consult legal counsel to review the applicable license when deploying a model.

Five Licensing Checks Before You Ship a Model

Understanding license terms becomes more important as open-weight models gain more adoption. This includes custom licensing terms and IP exposure as well as governance obligations and evolving regulations.

These conditions affect whether a model meets an organization’s needs. Reviewing them early enables teams to identify licensing obligations and potential risks before shipping a model. 

The following five checks highlight the conditions to evaluate before shipping a model:

Commercial Use and Revenue Thresholds

Check whether a model’s license permits commercial use without additional approval or imposes revenue and user-count thresholds. The Kimi K3 license imposes additional requirements on certain commercial deployments.

These conditions can restrict how organizations use a model when they exceed specific revenue thresholds. Companies also need to check whether these thresholds apply to individual products or aggregate revenue across the organization and its affiliates as this distinction can affect licensing obligations.

Hosted Inference and MaaS Restrictions

Some models’ licenses impose additional conditions on hosted inference or third-party access through an API. Kimi K3’s license requires qualifying MaaS businesses to sign a separate agreement if their aggregate revenue with affiliates exceeds $20 million over a consecutive 12-month period.

So, before offering Kimi K3 to customers, a company needs to understand how its service is classified under the license. The way it provides the model to users defines whether an additional agreement is required.

Fine-Tuning and Modification

Check if the license allows you to fine-tune or modify the model and deploy that version. And look for any conditions that apply. The Kimi K3 license explicitly allows users to modify the model and create derivative works.

Permission to fine-tune a model does not necessarily imply that all uses of the modified model are unrestricted. For example, Kimi K3’s license imposes additional conditions on certain commercial uses of the model and its derivatives, including qualifying Model-as-a-Service businesses.

Redistribution, Attribution, and License Notices

Check the requirements for distributing a model or its derivatives, including license copies and attribution notices. The Kimi K3 license requires its copyright and permission notices to be included in all copies or substantial portions of the software. 

Distributors must include a copy of the license and mark modified files under the Apache 2.0 license. They must also retain the required notices in distributed derivative works. Companies must review the specific model license before redistributing it or its modifications.

Prohibited Uses and Additional Conditions

Check if your intended use case is within the model’s permitted uses and complies with its restrictions. Google’s Gemma restricts certain uses involving sensitive personal information and automated decisions that affect individuals’ rights.

These restrictions can influence whether a model is appropriate for a planned application, especially in sensitive areas such as healthcare and finance. Review the applicable prohibited usage policy and the model’s license to confirm that both permit the intended deployment.

Why Model Licensing Becomes an Enterprise Risk

Open-weight model deployments can involve more legal considerations than traditional software procurement or hosted AI services. They can create risks that extend beyond the license terms. IP uncertainty, security responsibilities, contractual protections, and changing regulatory requirements can further complicate enterprise deployment.

Several factors show how these risks can affect enterprise deployment:

• Licensing obligations can sometimes conflict with business goals. Some licenses impose copyleft provisions that contradict an organization’s goal of keeping proprietary software confidential. 
• Model access does not ensure IP protection. Permission to use a model does not guarantee that it is free from third-party IP claims. Warranty disclaimers and liability limits may leave enterprises with limited recourse against the provider.
• Contractual protections vary depending on the deployment approach. Managed inference providers may offer support and security controls. But enterprises must check the contractual terms to understand how their data is handled and who is responsible if something goes wrong.
• Evolving regulations add uncertainty. Changes to AI regulations and data transfer rules change where enterprises can deploy models and which providers they can use. An enterprise might deploy a model or choose a provider only to discover that regulatory requirements restrict that deployment in a particular jurisdiction.

How DeepInfra Supports Enterprise AI Inference

Enterprises selecting an inference platform must consider not only model access and infrastructure but also how providers manage their data.

Routing platforms send requests to various model providers where data-handling and retention practices can vary. Enterprises using these platforms may need to evaluate the policies of multiple providers.

DeepInfra takes an integrated approach to inference infrastructure by combining GPU infrastructure with purpose built networking and inference optimized software as a unified inference platform. This frees enterprises from managing the stack themselves or evaluating multiple model providers. 

DeepInfra’s optimization stack combines inference software, open-weight models, and GPU infrastructure. | Source

DeepInfra’s terms limit customer data retention to the duration necessary for processing requests with specified exceptions for operational metadata and legal or security needs. It also holds SOC 2 and ISO 27001 certifications and also allows customers to enter into a separate data processing agreement (DPA).

These measures cover important aspects of enterprise AI inference but do not replace the need for a license review. Companies still need to verify that a model’s license permits the intended use and evaluate the contractual terms associated with its deployment. 

Final Thoughts

Open weights provide access to model parameters but do not grant universal commercial rights. Reviewing license terms before deployment will help identify restrictions that may impact future business plans and avoid surprises. 

DeepInfra lets enterprises deploy models in production without requiring them to manage the entire underlying infrastructure. It provides a broad model catalog and owns and operates its own GPU infrastructure. Its terms also include a zero-data-retention policy, with specified exceptions.

Explore DeepInfra’s models to compare available models and identify options for your workloads.