A model download can feel like a transfer of power. The files are on your machine; you can choose where inference happens and examine the surrounding code. Yet the product you build still depends on data access, evaluation and the capacity to run the system reliably. Openness changes the choices available. It does not make those choices disappear.
Why it matters: “Open” is too broad a label to tell a team what it can inspect, modify or redistribute.
Read what is actually available
The Open Source Initiative’s AI Definition 1.0 sets out freedoms to use, study, modify and share an AI system. Its preferred form for modification includes information about training data, the code used to train and run the system, and model parameters. The definition also distinguishes a model from its weights. The Open Source AI Definition.
This is the OSI’s definition, not proof that every release marketed as open meets it. A weights download alone cannot answer what training information is available or which restrictions apply to use.
A useful comparison starts with the actual release. List the supplied artifacts, inspect the stated terms and identify the changes your team needs to make. Do not let a category label stand in for that work.
Separate the model from the service around it
Running a model gives a team some direct control over deployment, but a useful application needs more. It must retrieve the right material, manage tools and identify when an answer is inadequate.
For a hypothetical research product, the difficult asset might be a carefully maintained source library and a set of questions with expert-reviewed answers. Another team could use the same model weights without having either. That is a reason to examine the complete workflow when comparing products, not evidence that one kind of model will always win.
Anthropic’s evaluation guide treats the agent and its supporting system as a combined object of evaluation. Its distinction between a transcript and the resulting outcome is relevant regardless of where the model runs. Demystifying evals for AI agents.
Control has an operating cost
A self-managed deployment can offer choices about updates and infrastructure. Someone then has to maintain those choices. A hosted service can take on more of that work, while making the customer dependent on its service behavior and terms.
Those are trade-offs to test against a specific workload. For a small team, start with the control it genuinely needs: data location, model modification, portability or predictable versioning. Then compare the total work required to preserve it.
The useful dividing line is often visible in an ordinary failure. When the answer is wrong, can the team find the cause, change the system and verify that the change helped?
For a connected perspective, see The AI Research Stack: What Small Teams Actually Need.
Reporting & Sources
An analysis of deployment and product control, not a model ranking or interpretation of a particular license. Sources reviewed September 26, 2026.
