PRIVATE AI VS PUBLIC AI: KEY FACTS

An article published by Appian discussed the context on public an private AI and provides four things you should know about private AI vs. public AI:

Data Privacy: Private AI ensures your enterprise data remains yours. Hosting models trained on private data guarantees data privacy and optimization for your specific use case. On the other hand, public AI requires sharing your private data with the AI provider, raising concerns about data security and ownership.
Control: Private AI offers greater control, allowing customization of AI models to suit your organization’s needs. This results in higher model accuracy and the ability to update algorithms over time. In contrast, public AI vendors seldom provide customization options, leading to less tailored solutions and more manual intervention.
Cost: Public AI models are generally more cost-effective, leveraging pre-trained models and cloud resources from public cloud providers. However, in-house private AI models can be expensive, requiring a team of experts to build and maintain infrastructure. A platform approach to private AI can offset these costs.
Speed: Public AI services can be quickly deployed due to reliance on pre-trained models and readily available resources. In-house private AI models may take more time to collect data, develop, test, and validate before deployment. Yet, with a platform approach, fully trained AI models can often be deployed in minutes.