Legal & Compliance Guide
Open Source vs. Open Weights AI
The legal and architectural truth behind open AI releases. Understand the OSI standard, commercial usage caps, distillation restrictions, and how to stay compliant when self-hosting models.
Top Open Weights & Open Source Models License Comparison
Compare commercial use, user caps, synthetic data rules, and OSI compliance at a glance.
| Model & Publisher | License Name | Commercial Use | User Cap / Limits | Dataset Released? | Train Competing Models? |
|---|---|---|---|---|---|
DeepSeek-V3 / R1 DeepSeek AI | MIT License Permissive License | Full Commercial Completely permissive. No user threshold or synthetic data restrictions. | No user limit | Private | Allowed |
Llama 3.1 / 3.2 / 3.3 Meta AI | Llama 3.3 Community License Custom Open Weights | Conditional Free for standard business, but cannot use outputs to train non-Llama models. | > 700M monthly active users requires custom license | Private | Restricted |
Qwen 2.5 (0.5B to 72B) Alibaba Cloud | Apache 2.0 (most sizes) / Tongyi Permissive License | Full Commercial Apache 2.0 for 0.5B to 32B models. Full commercial freedom. | > 100M MAU for certain 72B variants | Private | Allowed |
OLMo 2 (7B / 13B) Allen Institute for AI (Ai2) | Apache 2.0 (Code, Weights & Dolma Data) Permissive License | Full Commercial 100% OSI-compliant Open Source AI: weights, code, and full Dolma dataset released. | No user limit | Full Data (Dolma) | Allowed |
Mistral NeMo / 7B v0.3 Mistral AI | Apache 2.0 Permissive License | Full Commercial Permissive Apache 2.0. Note that Mistral Large uses proprietary licensing. | No user limit | Private | Allowed |
Gemma 2 (2B / 9B / 27B) Google DeepMind | Gemma Terms of Use Custom Open Weights | Conditional Open weights with Google safety policy and acceptable use restrictions. | No user limit | Private | Restricted |
Phi-4 (14B) Microsoft | MIT License Permissive License | Full Commercial Standard MIT open software license. | No user limit | Private | Allowed |
1. Why “Open Weights” Is Not Automatically “Open Source”
In traditional software development, “open source” has a precise legal meaning established by the Open Source Initiative (OSI) in 1998: software whose source code is freely available for anyone to inspect, modify, and distribute for any purpose, commercial or private.
When generative AI exploded, corporate marketing departments began slapping the label “open source” on any model with downloadable checkpoint weights. However, a neural network's weights are analogous to a compiled binary executable (.exe), not the human-readable source code.
Weights Downloadable, Recipe Secret
You can download the numerical parameters, quantize them to GGUF/AWQ, and run them offline. But the training dataset, filtering algorithms, data weighting, and training code are private. You cannot rebuild or audit the model from source.
Weights + Code + Data Provenance
Grants the 4 freedoms: use for any purpose without discrimination, study the system's full data recipe and code, modify and fine-tune without restrictions, and redistribute. True reproducibility from scratch.
2. Breakdown of the Top Model Families and Their Real Terms
Commercial Threshold:If your monthly active users exceed 700 million on the date of the model's release, you must request a custom enterprise license from Meta.
Synthetic Data Clause:You may not use Llama materials or outputs to “improve any other large language model (excluding Llama 3.1 derivatives)”.
Commercial Freedom: Released under standard permissive MIT license. Absolutely zero user caps, revenue thresholds, or commercial restrictions.
Synthetic Data Clause: Completely unrestricted. You can use DeepSeek-R1 reasoning traces to distill and train your own proprietary models.
Commercial Freedom: Models from 0.5B to 32B are licensed under permissive Apache 2.0. Certain flagship sizes (72B) use the Tongyi Qianwen license with a 100M MAU threshold.
Synthetic Data Clause: Fully allowed under Apache 2.0.
Open Weights + Code + Data: The entire Dolma 3T token dataset, training configs, evaluation harnesses, and weights are public under Apache 2.0.
Full Reproducibility: You can legally audit training sources, inspect copyright provenance, and retrain from scratch.
3. The Enterprise & Founder Checklist Before Self-Hosting
Confirm if the license includes user thresholds (e.g. 700M MAU for Llama) or revenue reporting clauses before integrating into commercial products.
If using model outputs to train smaller internal models (LoRAs / SLMs), ensure the source model is licensed under MIT or Apache 2.0.
Verify how much VRAM the model requires at production context lengths. Use our VRAM Calculator to ensure your GPUs can handle the load.
Frequently Asked Questions
Technically, no. The Open Source Initiative (OSI) classifies Llama 3 as an 'open-weights' model under the Meta Community License. It does not meet the Open Source definition because it places conditions on commercial scale (entities with over 700M monthly active users must request a license from Meta) and prohibits using Llama outputs to train competing models.
Yes, for the overwhelming majority of businesses. Llama 3 is completely free for commercial products with fewer than 700 million monthly active users. Qwen 2.5 models up to 32B use standard Apache 2.0 with unrestricted commercial rights. DeepSeek-V3 and R1 use standard permissive MIT licensing.
Announced in late 2024 by the Open Source Initiative, OSAID 1.0 states that for an AI system to be open source, it must provide: (1) Model Weights, (2) Training Code, and (3) Data Information detailing the provenance, filtering, and characteristics of training data, granting users the freedom to use, study, modify, and share without discrimination.
Allen Institute for AI's OLMo (Open Language Model) is one of the premier examples. Ai2 released not only the model weights and inference code, but also their complete 3-trillion-token training dataset (Dolma), data curation pipelines, and training logs under Apache 2.0.
Certain licenses (including OpenAI's API terms and Meta's Llama Community License) explicitly forbid using model outputs to train, fine-tune, or distill competing non-family models. If your company builds synthetic datasets for smaller SLMs, choose models with MIT (DeepSeek) or Apache 2.0 (Qwen/Mistral) licenses.
Filter open models by exact hardware compatibility
Our VRAM Calculator lets you filter models by license type (open source, open weights, gated) and verify GPU fit.
Knowledge & Deep Dives
VRAM & Local AI Knowledge Hub
Everything you need to know about sizing hardware, understanding model architecture, and running local LLMs without out-of-memory crashes.