For companies considering adopting an artificial intelligence solution into their stack, the question they will face is, "Do we build on public AI rails, or invest in a private AI model?"
This strategic fork presents two viable pathways to adoption: an open-source cloud-based AI solution hosted on third-party systems, or a permissioned, customizable model running on proprietary infrastructure. For decision-makers, selecting an adoption pathway comes down to risk tolerance, regulatory demands, scalability, and long-term value.
Public AI Explained
Public artificial intelligence refers to general-purpose, often generative and open-source, solutions hosted by third-party providers accessible through API plugins. Public AI models are very large, boasting millions of parameters and broad inference capabilities for fast outputs. Public AI solutions are easy to adopt, with low frontend costs, as third parties assist companies in configuring public models to meet their unique needs.
However, public AI entails trade-offs in data security. While delivering fast, efficient outputs, public AI models process training data externally, using large context windows to share events across interactions for model retraining. For firms dealing with sensitive data, this raises concerns about data integrity, user privacy, and compliance.
Private AI Explained
Private AI, unlike public AI, is closed-source, deployed privately, and integrates directly within a company's proprietary stack. Private models typically live in air-gapped environments, such as on-premises or private clouds, that use the company's own data for training and keep everything in-memory with exclusively internal data processing.
Private AI is fully customizable, allowing models to be tailored to a company's exact security and compliance specifications. Private AI has initially steep upfront costs, but with configurable governance, private models are cheaper to deploy in the long run, especially at scale.
What to Choose and When?
When considering what adoption path to take, either private or public AI, companies should weigh the following factors:
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Data sensitivity & regulation: Companies operating within highly sensitive data-driven sectors, such as finance and healthcare, where data integrity, sovereignty, and compliance are strictly regulated, may benefit from private AI solutions.
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Usage volume & long-term plans: Companies implementing small-scale pilot programs to test AI viability within their stack may benefit from the fast-to-deploy, cheaper entry point of public AI models.
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Custom domain needs: If companies require specialized outputs for internal data processes, the fine-tuning and customization of private AI may be the way to go.
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Budget constraints: For companies with limited operating budgets, public AI models can provide a cost-effective means of integration and deployment.
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Governance, compliance, & IP protection: Companies with low risk tolerance that require auditability and complete control of AI automations should consider private solutions.
Conclusion
The decision about whether to adopt AI publicly or privately comes down to a company's unique needs. Speed and short-term cost can be delivered by a public AI solution, while data protection and user control can be delivered by private solutions.