Definition: An open-weight model is an AI model whose trained weights are published for download, so organizations can run, inspect and fine-tune it on their own infrastructure, subject to its license.
Open weights are not always open source. Many releases share weights but not training data or full training code, and licenses range from permissive terms such as Apache 2.0 to custom licenses with usage restrictions. Examples include the Llama, Mistral, Qwen and Gemma families.
Self-hosting gives control over data location, customization, version pinning and cost at high volume. It also means owning GPU capacity, serving, security patches and inference performance.
Teams compare open-weight and API models on their own evals, read licenses carefully, and often use quantization and LoRA adapters to run and adapt them efficiently. Many organizations mix both, using APIs for the hardest tasks and self-hosted models where data control or volume pricing matters most.
Ryz machine learning engineers deploy and tune open-weight models in your cloud, working on your team.
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