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[2602.13033] Buy versus Build an LLM: A Decision Framework for Governments
Summary
This paper presents a strategic framework for governments to decide between buying or building large language models (LLMs) for public sector applications, considering factors like sovereignty, safety, and cost.
Why It Matters
As LLMs become integral to public services, governments must navigate complex decisions about sourcing these technologies. This framework aids policymakers in aligning AI strategies with national goals, ensuring effective and secure implementation of AI in governance.
Key Takeaways
- Governments face strategic choices in sourcing LLMs: buy, build, or hybrid.
- Key factors include sovereignty, safety, cost, and cultural fit.
- Building capabilities can involve partnerships with public institutions and enterprises.
- The framework helps align AI strategies with specific national needs.
- Different applications may require different sourcing strategies.
Computer Science > Computers and Society arXiv:2602.13033 (cs) [Submitted on 13 Feb 2026] Title:Buy versus Build an LLM: A Decision Framework for Governments Authors:Jiahao Lu, Ziwei Xu, William Tjhi, Junnan Li, Antoine Bosselut, Pang Wei Koh, Mohan Kankanhalli View a PDF of the paper titled Buy versus Build an LLM: A Decision Framework for Governments, by Jiahao Lu and 6 other authors View PDF HTML (experimental) Abstract:Large Language Models (LLMs) represent a new frontier of digital infrastructure that can support a wide range of public-sector applications, from general purpose citizen services to specialized and sensitive state functions. When expanding AI access, governments face a set of strategic choices over whether to buy existing services, build domestic capabilities, or adopt hybrid approaches across different domains and use cases. These are critical decisions especially when leading model providers are often foreign corporations, and LLM outputs are increasingly treated as trusted inputs to public decision-making and public discourse. In practice, these decisions are not intended to mandate a single approach across all domains; instead, national AI strategies are typically pluralistic, with sovereign, commercial and open-source models coexisting to serve different purposes. Governments may rely on commercial models for non-sensitive or commodity tasks, while pursuing greater control for critical, high-risk or strategically important applications. This paper p...
