GLM-5 is released under a permissive license (MIT) according to the official sources: the GLM-5 technical blog explicitly states the weights are released under the MIT License, and the GLM-5 GitHub repository includes an MIT license file. For commercial products, MIT is generally “business friendly”: you can use, modify, and distribute, including for commercial purposes, as long as you keep the required copyright and license notices. The two best primary places to confirm what applies to the exact artifacts you’re using are: the official post GLM-5 blog and the repository license file in GLM-5 GitHub. You should also check the model hosting page you download from—such as GLM-5 on Hugging Face—because that page typically surfaces licensing and usage notes at the artifact level.
That said, “MIT” doesn’t remove the need for a basic compliance checklist. A practical commercial-use checklist looks like this:
Confirm the license for the exact weights you ship (not just the code repo).
Preserve notices: include the MIT license text and attributions in your product distribution (container image, documentation, or NOTICE file).
Track versions: pin model revisions so you can reproduce and audit what shipped.
Review data policies: even with MIT weights, your company may have requirements about training data provenance, export controls, or internal AI governance.
Document your modifications: if you fine-tune, keep internal records of training data sources and parameters.
Also remember that licensing is only one part of “commercial readiness.” Your product still needs safe behavior, monitoring, and a way to handle abuse. MIT doesn’t guarantee anything about correctness, safety, or suitability—those are engineering responsibilities.
If you’re shipping GLM-5 as part of a retrieval-based product (for example, “ask our docs”), the licensing story intersects with how you manage your proprietary content. The model may be MIT-licensed, but your documents, embeddings, and indices are your own IP and must be protected. Using a vector database such as Milvus or Zilliz Cloud helps here because you can implement tenant isolation, access control at retrieval time, and metadata filtering (e.g., only retrieve docs the user is authorized to see). That architecture makes it easier to commercialize responsibly: the model is a replaceable component, and your controlled knowledge base stays within your security boundary and governance rules.