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What “open-source AI” means in government
The term can refer to different parts of an AI system, and they are not interchangeable:
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- Model artifacts: the model’s weights or other downloadable files. Access to weights can allow an agency or supplier to run a model in a chosen environment, but it does not by itself establish that the training data, development process, or every component is open.
- License and reuse rights: the terms governing access, modification, redistribution, and use. Read the actual license for the specific model and version; do not infer that every government, commercial, or sensitive use is allowed merely because weights are available.
- Serving software: the software that loads a model and makes it available to applications. It may be open-source even when the model it serves has different access or licensing terms.
- Operational service: the hosted platform, infrastructure, support, security controls, and service commitments around the model. An open model can be accessed through a centrally operated or commercial service, so model openness does not tell you where prompts, outputs, or logs are handled.
Procurement and technical teams should identify which of these layers is open, who operates each layer, and what data each party can access. The U.S. Government Accountability Office’s 2026 report on AI acquisitions highlights clear licensing, portability, pricing transparency, market research, cross-functional teams, and knowledge transfer as procurement considerations.
Three ways agencies can use open models
| Route | What the agency gets | Where responsibility sits | Key questions |
|---|---|---|---|
| Shared government inference platform | A centrally operated service for connecting agency applications to models and related AI capabilities. | The platform operator runs the service; the agency remains responsible for its application, intended use, data classification, and required approvals. | Which models and features are currently available? What are the service’s data-handling terms, authorization scope, quotas, availability commitments, and integration limits? |
| Agency-controlled deployment | Models and serving software run in an environment selected or controlled by the agency, potentially on local infrastructure or an approved cloud. | The agency or its integrator takes on infrastructure, patching, capacity planning, access controls, monitoring, and operational support. | Can the team operate the system securely and reliably? What approvals apply to this environment, and how will the agency manage updates, incidents, and workload peaks? |
| Procured vendor or integrator | A supplier provides an operated service, integration, or deployment support using open models or software. | Responsibilities are divided by contract and service design; the agency must verify boundaries, oversight, and exit arrangements. | What models and licenses are involved? Where does data flow? What are the pricing, support, portability, knowledge-transfer, and exit terms? |
Shared government platform
A shared inference platform can reduce the need for each agency to build model-serving infrastructure. France’s DINUM describes Albert API as providing access to generative models, on-demand retrieval-augmented generation (RAG), project management, and usage tracking. Its documentation distinguishes experimentation from a production pathway for partner ministries: the production pathway has service commitments and higher quotas, while experimentation has lower quotas and no availability guarantee. Confirm the live terms before designing a service around them.
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DINUM’s security documentation describes SecNumCloud hosting and says the service does not retain conversation traces for covered requests or send data to the public internet. These statements apply to the service and the authorization scope described by DINUM; they are not general properties of open models or a substitute for checking whether a particular agency workload is covered.
Agency-controlled deployment
DINUM says agencies can deploy OpenGateLLM, the open-source platform behind Albert API, for local use. It also describes sharing secure GPU infrastructure and connecting to models hosted with tools such as Ollama and vLLM. French government material on Albert says hosting can be on SecNumCloud, a public cloud, or a local server depending on the sensitivity of the data processed.
Running a system locally can give an agency more control over its hosting boundary, but it does not automatically make the system secure, compliant, affordable, or easier to maintain. The agency or its integrator must plan for the full operating lifecycle, including patching, identity and access management, capacity, monitoring, and incident response.
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Vendor or integrator
A supplier can reduce the amount of internal engineering needed to operate or integrate an open model. The contract still needs to make the supplier’s role and data access explicit. GAO’s 2026 acquisition report recommends attention to market research and cross-functional acquisition teams, and identifies knowledge transfer, data and model portability, clear licensing, and pricing transparency as solicitation considerations. Include support and exit provisions so an agency can change providers or deployment routes without losing essential access or expertise.
Government examples: France, the United States, and Japan
France: Albert
France’s DINUM developed Albert to help administrative agents answer public inquiries. French official material describes it as using open models adapted for administrative needs, with modular components and hosting options that can vary with data sensitivity. DINUM’s current Albert API documentation lists model access and supporting AI capabilities, but available models and terms can change; verify the live catalog and service conditions when evaluating or procuring the platform.
United States: GSA and Meta
In September 2025, the U.S. General Services Administration announced a collaboration with Meta intended to facilitate federal agency access to Llama and open-source AI tools. That announcement describes an access route, not blanket approval for every agency, model, or use case. Agencies still need to complete their own security, licensing, and suitability reviews.
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Japan: procurement guidance
Japan’s Digital Agency announced national government guidance developed with other ministries to encourage generative AI use in administrative work while managing risk. It is a governance and procurement reference, not an endorsement of any particular model.
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What public-sector research can—and cannot—tell agencies
A 2026 study in Government Information Quarterly draws on interviews with 31 public-sector decision-makers in Australia, Canada, and Germany. Interview themes include how existing contracts and security reviews may make proprietary services easier to adopt, alongside interest in control and air-gapped deployments for open-source options. These interviews describe perspectives from those participants; they are not a representative survey of all agencies or jurisdictions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to compare alternatives for an agency workload
Compare realistic options against the same task, data classification, and operating assumptions. An open model should not get a pass on quality or risk simply because it can be inspected or hosted internally.
| Evaluation area | What to establish |
|---|---|
| Task quality and language fit | Test representative agency tasks, terminology, and languages. Measure accuracy and failure modes, and decide how staff will detect and handle errors or hallucinations. |
| Data control and hosting boundary | Map where prompts, outputs, logs, and retrieval sources are processed, stored, or transmitted. Confirm whether any data can leave the agency boundary and under what conditions. |
| Security and approvals | Identify applicable security reviews, authorizations, identity and access controls, audit logging, incident response duties, and update ownership. Assess the actual service or deployment, not just the model. |
| Licensing and reuse | Review the specific model and software licenses, including restrictions relevant to government, commercial, or sensitive use. Confirm rights to modify, host, redistribute, or switch components. |
| Interoperability and portability | Determine whether the application can change models or hosting providers without major rework, and how data, configurations, and evaluation records can be moved. |
| Cost and operating capacity | Estimate infrastructure or service charges alongside staffing, compute, maintenance, support, evaluation, power, and cooling where relevant. Compare costs for the actual workload; local infrastructure is not inherently cheaper than a shared service. |
| Ongoing evaluation | Set performance measures and a recurring review process. Track changes to models, licenses, service terms, and workload needs over time. |
The UK Government’s AI procurement guidance advises agencies to explain why AI is relevant to the problem, remain open to alternatives, and plan ongoing evaluation. That is a useful starting point: first define the public-service task and assess whether AI is needed at all, then compare approaches on evidence rather than on the “open” label.
Procurement and deployment checklist
- Define the problem. Document the service task, expected benefit, affected users, and non-AI alternatives considered.
- Classify the data. Establish the sensitivity of prompts, generated outputs, logs, and retrieval sources, and define what may leave the agency boundary.
- Verify the components. Record the exact model and software versions, model artifact availability, license terms, and any relevant use restrictions.
- Set security ownership. Assign responsibility for review and authorization, identity and access management, audit logging, incident response, patching, and updates.
- Evaluate with representative work. Test agency tasks and languages, examine error handling, and retain appropriate human review for consequential decisions.
- Specify contract protections. Address pricing transparency, clear licensing, portability, knowledge transfer, performance measures, support, and exit assistance.
- Budget for operations. Include staff, compute, capacity, maintenance, evaluation, and support; for local hardware, account for procurement rules, power, and cooling as well.
- Schedule reassessment. Recheck task performance, security posture, service terms, licenses, and model availability as the system and its requirements change.
Model catalogs, licenses, service terms, and security eligibility can change. Confirm them with the relevant government platform operator, supplier, or license holder at the point of evaluation and procurement.
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