Buy versus Build an LLM: A Decision Framework for Governments
Jiahao Lu, Ziwei Xu, William Tjhi, Junnan Li, Antoine Bosselut, Pang Wei Koh, Mohan Kankanhalli

TL;DR
This paper offers a strategic framework for governments to decide whether to buy, build, or adopt hybrid approaches for LLM deployment, considering factors like sovereignty, safety, cost, and capability.
Contribution
It introduces a comprehensive decision framework evaluating buy versus build options for LLMs tailored to government needs and societal goals.
Findings
Framework evaluates sovereignty, safety, cost, and capability.
Domestic development can leverage public institutions and ecosystems.
Guidance for policy-makers on aligning LLM strategies with national priorities.
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsEthics and Social Impacts of AI · Artificial Intelligence in Law · Artificial Intelligence in Healthcare and Education
