Why your enterprise AI strategy needs both open and closed models: The TCO reality check

This article is part of VentureBeat’s special issue, “The Real Cost of AI: Performance, Efficiency and ROI at Scale.” Read more from this special issue.

For the last two decades, enterprises have had a choice between open-source and closed proprietary technologies.

The original choice for enterprises was primarily centered on operating systems, with Linux offering an open-source alternative to Microsoft Windows. In the developer realm, open-source languages like Python and JavaScript dominate, as open-source technologies, including Kubernetes, are standards in the cloud.

The same type of choice between open and closed is now facing enterprises for AI, with multiple options for both types of models. On the proprietary closed-model front are some of the biggest, most widely used models on the planet, including those from OpenAI and Anthropic. On the open-source side are models like Meta’s Llama, IBM Granite, Alibaba’s Qwen and DeepSeek.

Understanding when to use ...