Enterprise vs Public AI Model Selection
Tags: Frameworks
Overview
A central procurement decision is whether to use an enterprise or public AI model. The choice affects governance, security, IP exposure, and contractual leverage.
Pros and Cons
- Pros: Enterprise‑grade security; enhanced scalability; custom admin controls; greater IP protection and control over inputs.
- Cons: Higher cost; greater governance burden.
- Pros: Often cheaper or free; benefit from broader training inputs.
- Cons: Potential data and IP leakage; little/no control or auditability; one‑sided terms of service.
Critical Considerations
- Use of prompts and inputs for training: Enterprise offerings generally agree not to use customer inputs for training without consent. Public models often lack this restriction in standard terms.
- Security and compliance: Organizations may need to configure enterprise dashboards to meet regulatory/contract obligations.
- Acceptable use and training: Employee training and acceptable use policies help prevent sensitive data from being provided to public models.
- Third‑party dependencies: Some AI applications rely on third‑party models or inference providers. Vendors may only “pass through” third‑party representations rather than make their own commitments.
Gating Questions for Vendors
- What type of AI system is offered and for what business purpose?
- What data sources trained the model (proprietary, licensed, synthetic, third‑party)?
- What governance, provenance, and lawful sourcing documentation is available?
- How often are models audited, tested, or updated?
- What documentation demonstrates compliance with laws, regulations, or contracts?
Selecting between enterprise and public models should align with risk appetite, confidentiality needs, and intended deployment context.
Sources:- IAB_AI_Intellectual_Property_and_Transactions_Digital_Advertising_Playbook_December_2025.pdf
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