The Tool Desk
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What “open-weight” and “hosted” mean
An open-weight model is one whose trained parameters—the weights—are made available for others to download or use, subject to the applicable license. That does not necessarily make the training data, source code, development process, or safety evaluations public. A hosted model is accessed as a service operated by a provider; customers typically send requests to the provider rather than operating the provider’s model weights themselves.
These terms describe different things. “Open-weight” concerns what is released; “hosted” concerns who operates the service. A model’s release status alone does not tell you how capable it is, how effective its safeguards are, or how securely a particular deployment is run. The International AI Safety Report 2025 describes trade-offs between openness and centralized operation, not a categorical safety winner.
How the arrangements compare
| Question | Open-weight deployment | Hosted deployment | What to establish |
|---|---|---|---|
| What can you inspect? | Weights may be examined or adapted, subject to the license and the information released with them. Weights alone do not disclose every part of model development. | Customers typically cannot inspect provider-held weights directly. | Exactly what is public or contractually disclosed: weights, architecture information, usage information, training-data summaries, and evaluation results. |
| Who controls updates? | The developer can publish a new version, but downstream operators choose whether and when to adopt it. | The provider can roll out versions centrally, subject to its service and update practices. | Who sets the update schedule, communicates changes, and handles an incident involving a model version. |
| Who controls operation? | The operator can choose hosting and configuration and may be able to modify the model; that flexibility also brings deployment responsibilities. | The provider operates the service and controls its version rollout; the customer relies on the provider for those functions. | Who can monitor the system, restrict access, apply mitigations, and respond to failures. |
| How do safeguards hold up? | Safeguards can be changed or removed after release. Wider access can also support scrutiny and safety research. | Provider controls can be applied centrally, but their effectiveness depends on the provider’s design and enforcement. | What mitigations were tested and how they perform against bypass attempts and misuse. |
| Who secures the system? | The operator must secure model files, infrastructure, access, and data. | The provider secures its service; the customer remains responsible for its own integrations, credentials, and data flows. | How confidentiality, integrity, availability, access control, and logging are handled across the whole deployment. |
The European Commission captures the safety tension in its official Q&A: “open-sourcing advanced general-purpose AI models may indeed yield significant societal benefits, including through fostering AI safety research; at the same time, when such models are open-sourced, risk mitigations are more easily circumvented or removed.” Centralized hosting can make a fix easier to distribute through that service, but it does not prove that the service is safer or that every customer’s risks are addressed.
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Who is accountable when something goes wrong?
Accountability should be assigned by role for the concrete system and use case, not inferred from whether weights are public or an API is hosted. More than one actor may have responsibilities.
- Developer or provider: Identify who created or supplied the model, what documentation and evaluations are available, how known issues are communicated, and who handles model-level changes or incidents.
- Deployer or operator: Identify who selected the model, configured it, connected it to other systems, set access and monitoring controls, and decides whether the system is appropriate for its intended use.
- Downstream user: Identify which users can act on outputs, what training or review is required, and how errors or harmful outputs are reported and escalated.
For a hosted service, contracts and service documentation help clarify what the provider handles and what remains with the customer. For an open-weight deployment, the operator may have more direct control over configuration and infrastructure, while also taking on more of the work of securing and maintaining them. The legal duties of each party depend on the system, the party’s role, and the jurisdiction.
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What the EU AI Act’s open-source exception does—and does not—cover
The European Commission’s explanation of Article 53(2) describes a limited exception to specified documentation duties for providers of general-purpose AI (GPAI) models. It applies when the model is released under a qualifying free and open-source license and its weights, architecture information, and usage information are publicly available. Merely making weights available does not establish that these conditions are met.
- The exception does not apply to GPAI models with systemic risk.
- Qualifying providers remain subject to copyright-policy and training-data-summary requirements, according to the Commission.
- It is not a general exemption from the AI Act, and hosted status alone does not determine who has legal duties.
The Commission says GPAI provider obligations began applying on 2 August 2025 and that its enforcement powers for those obligations apply from 2 August 2026. Its provider guidelines explain the Commission’s interpretation and are non-binding. Applicability depends on the model and the parties’ roles; confirm current requirements for a real deployment with qualified legal advice.
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Use risk management and security controls for either approach
NIST’s AI Risk Management Framework (AI RMF) is intended for voluntary use. It helps organizations incorporate trustworthiness considerations into AI design, development, use, and evaluation; it is guidance, not a law or a guarantee of safety. NIST says AI RMF 1.0 is being revised.
NIST also emphasizes that AI systems share security concerns with other information systems. A practical review should include the underlying software and hardware as well as the model and its configuration. NIST’s developing Control Overlays for Securing AI Systems include model weights and configuration settings, supporting an operational focus on protecting those artifacts and the systems around them.
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- Confidentiality: Determine what data enters the model, where it is processed, and who can access prompts, outputs, logs, and model files.
- Integrity: Control who can change model artifacts, configurations, integrations, and safety settings; keep changes traceable.
- Availability: Plan for service outages or infrastructure failures, and establish who can restore operation or provide an alternative.
- Incident handling: Set out who monitors issues, reports them, applies mitigations, and communicates changes to affected users.
How to choose for a specific use case
Choose based on the controls and responsibilities your organization can actually sustain, rather than treating openness or hosting as a safety label.
- Define the use and consequences of failure. Identify who relies on the output, what decisions it influences, and what harm an error or misuse could cause.
- Map what you can verify. For an open-weight option, check the license and accompanying information. For a hosted option, review available documentation, service terms, security practices, and update communications.
- Assign operational ownership. Name the people responsible for access, monitoring, incident response, integrations, and changes to the system.
- Check whether safeguards fit the use. Examine relevant evaluations and mitigations, and determine how misuse or bypass attempts will be detected and handled.
- Confirm legal duties for the actual roles. Establish which rules apply to the model, provider, deployer, and intended use in the relevant jurisdiction.
If you cannot inspect or obtain enough information to assess a consequential use, or cannot assign someone to manage its risks, neither deployment model resolves that gap by itself.
Quick Recap
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