Why Enterprises Still Hesitate on Open-Source AI

“Open source has been a huge advantage for business,” said Arthur Mensch, CEO of Mistral AI. “The idea of being able to deploy locally and wherever you want has changed the way we look at the technology”. That optimism is reflected in the pace of recent releases.

For example, models like DeepSeek’s latest V3.1, delivering coding accuracy on par with proprietary rivals while being up to 68 × cheaper. This should be prompting a wave of enterprise uptake. Yet a Capgemini survey reveals that three-quarters of executives still favor closed AI systems, citing support, security, and integration concerns

The reasons aren’t just about performance. “Companies love experimenting with open source models because there's no upfront cost and the performance is often impressive. But when they're ready to move to production… you're on your own if there's any copyright issue with generated content,” AI strategist Biju Krishnan told AIM Media House. “Compare that to using something like Google's Gemini, where full copyright protection comes standard.”

Concerns about stability reinforce the bias toward closed systems. Tools like LangChain illustrate the gap between experimentation and enterprise readiness. “It became clear that while LangChain was great for prototyping, it wasn't enterprise ready,” Krishnan said. “Interestingly, the same team then launched LangGraph and LangSmith as proprietary SaaS solutions, specifically targeting enterprises that needed stability and support.”