As more companies adopt generative AI across various business functions, a growing number of technology leaders are turning to open-source tools to strengthen their AI infrastructure.

How Companies Are Leveraging Open Source AI

Our recent observations indicate that open-source AI is increasingly integrated into corporate tech stacks, especially in areas where it fits seamlessly with existing systems and security protocols. Over half of the surveyed organizations reported using open-source solutions in areas such as data management, AI models, and development tools.

Interestingly, open source is less commonly applied to areas like model customization (for example, fine-tuning) and hosting or inference computing. This trend could be due to the relative novelty of certain open-source inference tools or because companies prefer internal or proprietary resources for specialized use cases.

Looking Forward: Growth and Experimentation

The open-source AI ecosystem experienced notable expansion in 2024, with more models being released and improvements narrowing the gap with proprietary options. Most organizations—about three-quarters of those surveyed—anticipate increasing their reliance on open-source AI in the coming years, signaling a continued trend toward experimentation and adoption.

Driving Innovation Through Collaboration

Open-source models play a pivotal role in accelerating AI innovation. By enabling collaboration, they reduce duplication of effort and allow developers to build on each other’s work. This approach is expected to influence key AI trends, including privacy-focused edge applications powered by compact language models and advanced reasoning models requiring higher inference-time computation.

The Future of Open Source AI

Open-source AI is emerging as a central component of modern AI strategies. Demand for open-source technologies spans industries, regions, and different layers of the tech stack. As adoption grows, organizations stand to benefit from lower development costs and the ability to deploy tailored AI systems, even on edge devices.

To fully capitalize on these advantages, businesses must ensure they can navigate the technical and legal challenges associated with open-source AI. Moreover, because experienced developers are often the main contributors to open-source projects, investing in team training is essential.

Finally, as seen in cloud and software sectors, a hybrid approach combining open-source and proprietary technologies is likely to become standard, allowing organizations to harness the best of both worlds across their AI infrastructure.