As business leaders integrate generative artificial intelligence into their operations, they face a critical decision: should they develop AI capabilities using open-source models or depend on proprietary, closed-source options? Recognizing the consequences of this decision can determine whether an organization gains a lasting competitive edge or makes a costly strategic error.

But what does "open source" really mean?

The Open-Source Initiative (OSI) defines open software as that which grants users the freedom to use it for any purpose, examine how it functions, make modifications, and share both the original and altered versions. In the context of AI, truly open-source systems would include the model architecture (the framework for how AI processes data), training data methodologies (documentation of how data was selected and used in training), and model weights (numerical values representing the learned knowledge of the AI).

However, very few AI models fully meet the OSI’s criteria for openness.

The Spectrum of Openness

While completely open-source models offer full transparency, most developers are reluctant to publish their entire codebases, and even fewer disclose the datasets used for training. Many so-called foundation models — the largest generative AI systems — are trained on data with questionable or potentially infringing copyright status.

More common are open-weight models, which provide public access to model weights but not the full training data or architecture. This enables faster deployment and experimentation with limited resources, although it hampers efforts to identify biases or enhance accuracy without full visibility.

Some companies follow a staged openness approach. They may release older versions of proprietary models once newer ones are available, offering partial insight into the architecture while limiting access to cutting-edge developments. Even then, training data is seldom made public.

Making the Right Choice

Whether an enterprise opts for a proprietary system like GPT-4o or a partially open one like LlaMA 3.3 depends largely on its specific needs. Many organizations end up using a combination of both open and closed models.

A key consideration is where the model will be hosted. For regulated sectors such as finance, where sensitive data cannot leave internal systems due to compliance requirements, open-source models are often the only feasible choice. Since proprietary model creators must protect their intellectual property, those models are typically accessible only via remote APIs.

Open-source models, by contrast, can be deployed either on-premises or in the cloud.

Both types of models can be fine-tuned for specific applications, but open-source models allow for greater customization and deeper integration. Moreover, the data used during fine-tuning doesn’t have to leave the company's infrastructure. Fine-tuning proprietary models requires less technical expertise but must occur in the cloud.

Cost and response time also play a role. Proprietary providers usually operate at scale, enabling fast, consistent performance — crucial for high-volume consumer-facing tools like chatbots or virtual assistants handling millions of interactions daily.

Although open-source AI can be more cost-effective over time, achieving comparable speed and reliability demands substantial investment in infrastructure and skilled personnel.

Regulatory compliance is another important factor. The European Union’s Artificial Intelligence Act imposes stricter transparency and accountability standards on proprietary AI systems. However, proprietary vendors often take on much of the compliance burden, reducing pressure on businesses. In the U.S., the National Telecommunications and Information Administration (NTIA) is exploring risk-based frameworks for evaluating AI openness.

Security is also a concern. With proprietary models, companies rely on the provider to ensure robust security. But this lack of visibility can conceal vulnerabilities, leaving organizations dependent on vendors to detect and fix issues.

In contrast, open-source models benefit from global communities of security experts who quickly identify and resolve threats.

Nonetheless, many businesses favor the simplicity of API-based proprietary models for quick development. And for customer-facing products, proprietary models offer ease of integration and high responsiveness.

Will Open-Source Models Surpass Closed Ones?

An even bigger question looms over the future of open and closed models. As open-source models improve in performance — sometimes matching or even surpassing top proprietary models — the long-term economic viability of closed models becomes uncertain.

China is aggressively pursuing an open-source strategy, lowering costs to compete with Western firms like OpenAI. By openly sharing research, code, and models, China aims to deliver advanced AI capabilities at a fraction of the price of proprietary alternatives.

Actionable Insights for Business Leaders

Recall Betamax, Sony’s proprietary video format from the 1970s. It ultimately lost to the more open VHS format — a fate some predict for closed AI models as open-source alternatives rise.

Leaders must clearly define their AI objectives — whether focused on efficiency, innovation, risk mitigation, or regulatory compliance — and let these goals shape their model selection and implementation strategies. They might, for instance, tap into open-source communities for innovation and prototyping, while relying on proprietary systems for secure, mission-critical tasks.

Strategic collaboration with external partners and the smart use of both open and closed models can enable responsible innovation and maintain competitiveness.

Ultimately, business leaders must understand their operational needs, data sensitivities, and technical capacities — and choose accordingly. Choosing between open-source and proprietary AI isn't a yes-or-no decision, but rather a matter of finding the right point along a spectrum from closed to fully open.

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