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Choose by separating three decisions: the model’s license and openness, where inference runs, and who operates the service. An open-weight model can be self-hosted, deployed on a dedicated endpoint, or consumed through a commercial platform. Conversely, a commercial platform may expose both proprietary and open-weight models. “Open” and “EU-hosted” labels alone do not establish licensing rights, GDPR compliance, data residency, or AI Act status.
For a European organization, the defensible choice is the one whose model rights, technical architecture, contractual controls, operational capability, and AI Act responsibilities match the intended use. The guide below provides a way to make that choice without treating marketing categories as legal or technical conclusions.
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The three axes buyers must keep separate
Most procurement debates collapse different questions into “open source versus commercial.” That shortcut creates avoidable risk. Evaluate the following axes independently:
1. Model license and openness
A model may publish its parameters or weights while imposing conditions on commercial use, redistribution, modification, attribution, acceptable use, or downstream services. “Open weights” describes access to parameters; it does not automatically mean the model is open source under the relevant legal definition. Read the license that governs the exact version you intend to use, including any separate terms for code, weights, training data, or safety components.
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2. Deployment and inference control
Inference can run in your own data centre, in a cloud account you control, on a dedicated hosted endpoint, or in a shared provider service. The same weights can therefore produce very different data flows, logging arrangements, support access, and operational responsibilities.
3. Service and support model
A community project may provide code and documentation but no uptime commitment. A commercial platform may operate the endpoint, scale capacity, monitor abuse, provide support, and contractually accept defined responsibilities. Commercial operation does not tell you whether the underlying model is proprietary or open-weight.
| Question | What it actually determines |
|---|---|
| Can we access or modify the weights? | Model rights under the applicable license; not automatically your hosting or support arrangement. |
| Where does a prompt run? | Inference location, network path, subprocessors, logs, backups, and support access. |
| Who keeps the service available? | Operational ownership, incident response, upgrades, scaling, and exit obligations. |
| Who is legally responsible? | Roles under the AI Act and data-protection contracts, which depend on the use and configuration. |
What “open-source” means under the EU AI Act
The European Commission describes a narrow exemption for certain general-purpose AI (GPAI) models released under a free and open-source license. A provider may qualify only when the license permits access, use, modification, and distribution without monetisation; the provider publicly makes the model’s parameters (including weights), architecture, and usage information available; and the model is not classified as a GPAI model with systemic risk.
Where those conditions are met, the described exemption covers specific duties: technical documentation for authorities, documentation intended for downstream AI system providers, and appointment of an authorised representative for a provider outside the EU. It is not a general exemption from the AI Act.
Obligations that remain
- The provider still has to comply with the copyright-policy obligation.
- The provider still has to publish a summary of the training data.
- A model with systemic risk does not receive the described open-source exemption; systemic-risk obligations apply even when the model is released openly.
The Commission says GPAI-provider obligations began applying on 2 August 2025. Its provider guidelines also explain that significant modifications can cause provider obligations to apply to the party making the modification. Do not infer your role from the word “open” alone: determine who placed the model on the market, who substantially modified it, and who supplies the downstream system.
The Code of Practice is voluntary
The Commission presents the GPAI Code of Practice as a voluntary practical tool for demonstrating compliance. Its signatory page was shown as last updated on 7 October 2026. Signing is not a blanket certification, does not replace statutory duties, and does not prove that a particular deployment is compliant.
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Open or self-managed versus commercial managed platforms
The following comparison is a decision framework rather than a vendor ranking. A product’s current documentation and contract control the result.
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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →| Decision axis | Open or self-managed approach | Commercial managed approach | Verify before approval |
|---|---|---|---|
| License and model rights | May provide weights and broader adaptation rights, subject to the actual license. | May combine proprietary models with open-weight options behind one service. | Use restrictions, commercial rights, redistribution, fine-tuning, attribution, and version-specific terms. |
| Infrastructure control | Your organisation chooses hardware, network boundaries, runtime, and update schedule. | The provider manages more of the hosting and inference layer. | Hosting region, inference region, backups, logs, system data, support access, and subprocessors. |
| Operations | You handle capacity, scaling, security patches, observability, upgrades, and incident response. | The vendor supplies some or all of those functions, often with support and service commitments. | Staffing, uptime or SLA terms, maintenance windows, incident notices, and exit assistance. |
| Data handling | Control depends on your environment, identity controls, telemetry, and administrator discipline. | Retention, processing, and residency follow provider-specific terms and configuration. | DPA roles, retention and deletion, training use, transfer mechanisms, access controls, and subprocessors. |
| Model choice | You can select eligible weights and runtimes within hardware and license constraints. | A common API may expose proprietary and third-party models with provider safety controls. | Availability, version pinning, evaluation evidence, safety features, portability, pricing, and performance evidence. |
| AI Act roles | You may become the provider of a modified model or system, depending on what you do. | Hosted access alone does not automatically make you a GPAI provider, but integration, repackaging, or significant modification can change the analysis. | Provider, system-provider, deployer, intended purpose, risk category, and modification facts. |
Deployment patterns and their practical consequences
Self-hosted inference
Your organisation runs the model in its own controlled environment. This can simplify the boundary around prompts and outputs because the inference server, network, logs, and backups are yours to configure. It also transfers responsibility to your team: GPU capacity, model-serving software, patching, authentication, abuse controls, monitoring, disaster recovery, and secure decommissioning all need an owner.
Self-hosting does not cure a restrictive license. Confirm that the license permits the intended commercial use, internal users, fine-tuning, redistribution of an adapted model, and any hosted service you plan to offer. Keep an inventory of the exact model, weights, tokenizer, runtime, adapters, and configuration so that a later upgrade does not silently change your rights or risk assessment.
Dedicated hosted endpoint
A dedicated endpoint can reduce infrastructure work while offering more isolation than a shared API. Ask whether “dedicated” refers to hardware, a tenant boundary, a network path, or simply a billing tier. Verify where requests are processed, where logs and backups reside, which staff or subprocessors can access them, and what happens during support or abuse investigations.
Shared commercial API or platform
A managed API is often the fastest route to production. The provider may handle scaling, model updates, safety filters, and operational monitoring. In return, your control depends on service terms and product settings. Confirm retention defaults, training use, regional processing, model and endpoint eligibility, rate limits, outage procedures, and whether you can export prompts, evaluations, configurations, and application state if you leave.
Hybrid architecture
Many organisations route low-sensitivity workloads to a managed service and keep regulated or confidential workloads in a self-managed environment. Define routing rules before launch: data classification, approved models, geographic constraints, fallback behaviour, and audit logs. A hybrid design can reduce concentration risk, but it introduces policy and observability complexity at every hand-off.
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Why “EU-hosted” is not a complete residency answer
Residency is a property of a particular product, endpoint, model, processing mode, and request type. Storage at rest and regional processing are different commitments. A platform may keep eligible data in the European Economic Area (EEA) plus Switzerland while still processing some requests elsewhere, or exclude certain tools, system data, abuse-monitoring flows, or other categories from the residency promise.
For example, OpenAI’s API documentation describes EEA and Switzerland residency for supported services and distinguishes storage from regional processing. It states that regional storage does not automatically imply regional processing and that eligibility depends on the model, endpoint, tools, and configuration. Treat the published endpoint and model table as a product fact that can change; check it again when selecting a model and when contracts renew.
OpenAI’s business privacy documentation says data from listed business products and its API platform is not used to train or improve models by default. That is the provider’s description of its practice, not an independent determination that your processing complies with the GDPR. Your legal basis, instructions, retention schedule, transfer analysis, security measures, and use case still matter.
Hugging Face’s Inference Endpoints security documentation states that endpoint payloads and tokens are not stored by Hugging Face and that a GDPR data-processing agreement is available through Enterprise Hub. Its FAQ describes deployments across cloud regions. Those statements apply to the named service and published configuration; they should not be generalized to every product, partner route, or deployment option. Obtain the terms for the exact endpoint you will operate.
Contract and data-flow due diligence
Complete these checks for each proposed model and endpoint, then preserve the answers with the architecture record.
- Map the parties. Identify the model provider, system provider, deployer, your organisation’s processor or controller role, and any subprocessors.
- Freeze the model identity. Record the model name, version, weights, tokenizer, runtime, safety configuration, and update policy.
- Read the license. Check commercial use, modification, fine-tuning, redistribution, attribution, acceptable-use restrictions, and obligations triggered by offering the model as a service.
- Draw the data flow. Include prompts, outputs, embeddings, uploaded files, telemetry, abuse-monitoring data, support tickets, logs, backups, and administrator access.
- Separate storage from processing. Confirm the region for inference, temporary buffers, persistent storage, backups, and disaster-recovery copies.
- Check request eligibility. Verify that the selected model, endpoint, tool call, batch mode, and account configuration are all covered by the stated residency and retention terms.
- Review the DPA and service terms. Look for documented roles, instructions, subprocessors, international transfers, deletion, security incidents, audit rights, and assistance with data-subject requests.
- Set retention deliberately. Disable optional logging where possible, define a deletion period, and test that deletion reaches derived stores and backups on the promised schedule.
- Control access. Use least-privilege identities, separate development and production credentials, restrict support access, and log administrative actions.
- Evaluate the model. Test quality, safety, bias, security, latency, and failure modes on representative data before approving a production use.
- Plan change management. Decide whether automatic model updates are allowed, how releases are evaluated, and who approves a version change or a significant modification.
- Prepare exit and migration. Keep prompts, evaluation sets, system instructions, adapters, configuration, and application data in formats that can move to another runtime or provider.
Assigning AI Act responsibilities in real projects
Start with the intended purpose and the actual system, not the supplier’s marketing category. A company using an API to add summarisation to an internal workflow may have different duties from a company placing a general-purpose model on the market, substantially modifying it, or embedding it in a regulated high-risk system.
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Questions for the model layer
- Who first made the GPAI model available in the EU market?
- Was it released under terms and documentation that meet the conditions for the limited open-source exemption?
- Has your organisation changed the model significantly enough to become a provider for the modified model?
- Does the model have systemic-risk classification or obligations that override the exemption?
Questions for the application layer
- What is the system’s intended purpose and user population?
- Is the output used to make or support decisions affecting people, safety, employment, access to services, or other regulated interests?
- Which organisation controls instructions, human oversight, monitoring, incident handling, and user notices?
- What technical documentation, logging, transparency, or risk-management duties apply to the finished system?
Document the reasoning and obtain legal review where the classification is uncertain. A European location, an open license, or a commercial contract does not by itself answer these questions.
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Choose self-managed or hybrid when control is the primary requirement
This route suits workloads where network isolation, custom security controls, offline operation, or deep model adaptation outweigh the cost of specialist operations. Budget for hardware capacity, on-call coverage, patching, evaluations, and a reproducible deployment pipeline. If the team cannot operate those controls continuously, nominal ownership may provide less real protection than a well-contracted managed service.
Choose managed inference when speed and operational coverage dominate
A commercial platform is often appropriate when the organisation needs rapid integration, elastic capacity, managed upgrades, or support that it cannot staff internally. Make the decision conditional on acceptable retention, processing locations, subprocessors, version controls, incident commitments, and an exit plan. Do not treat a provider’s privacy statement as a substitute for the DPA and your own data-protection assessment.
Use a split policy for mixed workloads
Classify data before selecting a route. Public or low-sensitivity content may use a managed API, while confidential, regulated, or contractually restricted data uses an approved private endpoint or self-managed model. Enforce the policy in code and at the gateway rather than relying on employee judgement at the prompt box.
Common failure modes and corrections
Failure: calling any model with public weights “open source”
Correction: cite the exact license and list the rights it grants and restricts. Use “open weights” when that is the accurate description.
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Failure: assuming EU storage means EU processing
Correction: verify storage, inference, logs, backups, tools, and support flows separately for the selected endpoint and model.
Failure: assuming a hosted model makes the customer the GPAI provider
Correction: map what your organisation modifies, places on the market, integrates, and controls. Apply the role analysis to the actual system and intended purpose.
Failure: relying on a privacy webpage without contractual validation
Correction: reconcile the provider statement with the DPA, service terms, technical configuration, retention settings, subprocessors, and transfer mechanism.
Failure: accepting automatic model changes
Correction: pin versions where possible, run regression and safety tests, record release approvals, and define a rollback path.
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Correction: preserve evaluation data, prompts, adapters, schemas, and observability data independently of the platform, and test a second runtime before a critical dependency forms.
A practical approval record
Before production, keep a short decision record containing:
- The use case, intended purpose, users, data classes, and geographic scope.
- The model license, version, provider, and any modifications.
- The deployment diagram with inference, storage, logging, backup, and support locations.
- The DPA, transfer mechanism, retention settings, subprocessors, and deletion procedure.
- The AI Act role analysis and the controls assigned to each party.
- Evaluation results, safety limits, human-oversight procedures, incident contacts, and rollback steps.
- The renewal trigger for rechecking documentation, endpoint eligibility, model terms, and regulatory guidance.
This record turns an abstract “open versus commercial” debate into an auditable choice. Revisit it whenever the model, endpoint, data class, provider terms, or intended purpose changes.
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