Neither open nor closed AI models are automatically more private, cheaper, or safer. Open-weight models let you run and adapt the model yourself, which can give you more control—but also makes you responsible for infrastructure, security, safeguards, and maintenance. Closed models are typically accessed through a provider’s service, shifting much of that operational work to the provider while making you more dependent on its policies and disclosures. The right choice turns on your data requirements, workload, and capacity to operate the system.
What “open” and “closed” mean
“Open” describes a range of release choices, not one guaranteed level of transparency. The European Data Protection Board (EDPB) distinguishes proprietary closed models, whose weights or source code are not publicly available and are usually accessed through an API or subscription, from open-weight models, whose trained parameters can be inspected, fine-tuned, or integrated by others. A model may publish weights but not its training data, code, or full documentation. Check what is actually released and the license; do not assume that “open-weight” means fully open-source.
Closed models keep their weights under provider control. The provider generally determines how the model is served and what access, documentation, and controls customers receive. This describes a common deployment distinction, not a guarantee that every company’s products work the same way.
The distinction matters in practice: the OECD reported that approximately 55% of commercially available foundation models in its studied dataset were open-weight as of April 2025. That dataset covered models made commercially available by one or more providers through an API endpoint; it is not the share of all models or deployed AI systems. The OECD describes its underlying AIKoD database as experimental, with data last updated April 30, 2025.
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Compare the trade-offs that affect your decision
| Decision area | Open-weight model | Closed model service |
|---|---|---|
| Data boundary | You may run inference on infrastructure you control or choose, but your security, access, retention, and compliance arrangements depend on how you operate it. | The provider operates the service. Your protections depend on its current data practices, endpoint settings, contract, and available controls. |
| Total cost | Compute, hosting, power, integration, maintenance, and staff time count toward the cost; hosted open-weight inference still has a hosting charge. | The provider operates the infrastructure, while you pay according to its service-specific pricing and terms. |
| Customization and portability | Weights can permit adaptation and deployment choices, subject to the model’s license and usage policy. | The provider retains control of the weights and serving system; customization is limited to the options it offers. |
| Safety responsibility | You must assess the exact model and any modifications, and provide appropriate safeguards and monitoring for your deployment. | The provider controls the deployed model and its safeguards; you rely on its evaluations and disclosures while still managing your use of the service. |
| Operations and support | You or a hosting partner handle deployment and ongoing operations; hands-on help varies by provider. | The service provider operates the model, with support and availability governed by its service terms. |
Which is more private?
Privacy depends less on the open/closed label than on where prompts and outputs go, who can access them, what is retained, and which contractual and technical controls apply. Local hosting can keep inference within infrastructure you choose, but it does not by itself secure that infrastructure, control staff access, or establish legal compliance. Open models may also expose personal information learned during training, and incomplete release of training data can limit scrutiny. The EDPB notes that modifications can introduce vulnerabilities or remove safety measures.
A hosted closed service is not necessarily unsuitable for sensitive data. For example, OpenAI’s platform documentation says API data is not used to train or improve its models unless a customer explicitly opts in. It also describes storage and processing according to service, endpoint, and region. Before using any hosted service, check the current terms for the specific endpoint, retention and deletion behavior, data residency, and eligibility for controls such as modified abuse monitoring or zero data retention.
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OpenAI also lists a SOC 2 Type 2 examination covering controls relevant to security, availability, confidentiality, and privacy for its API and ChatGPT business services, and says it maintains ISO/IEC 27001:2022 and ISO/IEC 27701:2019 certifications for specified business services. These are scoped provider statements, not a guarantee that a particular customer’s use or deployment meets its requirements. A buyer still needs to assess the relevant service, contract, and configuration.
Which is cheaper to run?
There is no established cost winner without comparing the same workload. Self-hosting avoids a per-request API meter but brings costs for suitable compute, capacity planning, power, integration, maintenance, and staff. A managed host charges to run open weights. A closed API shifts infrastructure operation to the provider, but charges and service terms vary. Compare equivalent quality, context length, volume, throughput, latency, uptime, and accounting period, and include engineering and operational work—not just the price of a token or a server.
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OpenAI says its gpt-oss models are not available through the OpenAI API, so OpenAI API pricing and rate limits do not apply to those weights. Running them is not cost-free: the operator or hosting provider pays the compute and operating costs.
What customization and control do open weights provide?
With open weights, an operator can choose where to serve the model and may adapt it using compatible tools. That can help when deployment location, integration, or model changes are important. The corresponding trade-off is responsibility for selecting and maintaining the serving stack, managing updates, and checking that adaptations behave as intended. Portability is also bounded by the license, usage policy, technical compatibility, and the effort of moving a deployment.
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Closed-model customers use the provider’s weights and serving system rather than taking possession of the model. OpenAI says it deploys its most powerful models as services, does not distribute their weights beyond OpenAI and its technology partner Microsoft, and offers third-party access through APIs. That is OpenAI’s stated approach, not a description of every closed-model provider.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Does open or closed mean safer?
No. Safety depends on the exact model and version, its evaluations, the safeguards around the deployment, and how it is used. OpenAI says gpt-oss underwent safety training and testing; its August 5, 2025 model card also notes that downstream systems may be built and maintained by many stakeholders and may need additional safeguards to reproduce system-level protections in OpenAI’s API and products. Those model-specific evaluations are not proof that every fine-tune, task, or deployment is safe.
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For an open-weight deployment, the operator should evaluate the model after adaptation, enforce relevant policies, monitor behavior, and account for modifications that weaken safeguards. In a closed service, the provider controls the deployed model and publishes the evaluations and system documentation it chooses to share. The customer still needs to determine whether those materials address its use case and what happens when failures occur.
OpenAI describes its system cards as documents intended to inform readers about factors affecting system behavior, especially responsible use. For either model type, inspect documentation for the specific model and version instead of treating a release label as a safety rating.
What gpt-oss shows about the distinction
OpenAI describes gpt-oss-120b and gpt-oss-20b as open-weight reasoning models. It says they are licensed under Apache 2.0 subject to the gpt-oss usage policy, can be run on operator-controlled infrastructure or through hosting providers, and can be adapted with open tooling. OpenAI says they are not offered through ChatGPT or the OpenAI API. Its Help Center names vLLM, Ollama, and llama.cpp among compatible inference stacks. Check the current license and usage terms before deployment.
| Model | Total parameters | Active parameters per token |
|---|---|---|
| gpt-oss-120b | 116.8 billion | 5.1 billion |
| gpt-oss-20b | 20.9 billion | 3.6 billion |
These figures come from the August 5, 2025 model card. They describe those models; they are not universal hardware requirements or a cost comparison. OpenAI characterizes gpt-oss deployments as self-managed and self-serviced and says it does not provide hands-on implementation or debugging help for self-hosted or third-party-hosted configurations.
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A practical checklist for choosing
- Set the data boundary. Decide where prompts and outputs may be processed, who may access them, and what retention, training-use, residency, and deletion terms you require.
- Price a matched workload. Compare the same volume, quality target, context length, latency, throughput, uptime, and accounting period. Include compute, hosting, integration, engineering, and operations.
- Check adaptation and portability. Confirm what can be modified, whether the license and usage policy permit your intended use, and what it would take to move between infrastructure providers.
- Assign safety ownership. Identify which evaluations apply to the exact model and version, who tests adaptations, who monitors misuse and failures, and who updates safeguards.
- Confirm operations and support. Decide who will deploy, debug, update, and respond to incidents, and verify the actual support and availability commitments for the option you plan to use.
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