Open-Source vs Proprietary AI: Which Should Businesses Choose?

When deploying generative artificial intelligence (AI), one of the most fundamental decisions businesses face is whether to choose open-source or proprietary AI models — or aim for a hybrid of the two.

“This basic choice between the open source ecosystem and a proprietary setting impacts countless business and technical decision, making it ‘the AI developer’s dilemma,’” according to an Intel Labs blog post.

This choice is critical because it affects a company’s AI development, accessibility, security and innovation. Businesses must navigate these options carefully to maximize benefits while mitigating risks.

What Is Open-Source AI?

Open-source AI consists of AI algorithms, tools and models that are free to use, modify and share publicly, according to the Open Source Initiative. These privileges apply to both the fully functional system and to its parts. Researchers should be able to inspect its components and study how the system works, per MIT Technology Review.

The goal is for the AI community at large to contribute improvements for everyone’s benefit.

Remember Linux? It was the open-source rival to Unix and major tech companies developed their own closed version of it, according to Meta. But Linux gained popularity and it is now the industry standard for cloud computing and operating systems for most mobile devices.

Key examples of open-source AI models (although some attach licenses that restrict certain uses):

Meta’s Llama family of models

Mistral AI’s Mistral family

Microsoft Phi family

DeepSeek’s V3 and R1

Falcon (U.A.E.’s Technology Innovation Institute)

BLOOM (BigScience)

Eleuther AI’s GPT-NeoX and GPT-J

Advantages of Open-Source AI

Transparency: Public scrutiny reduces the risk of hidden biases and unethical practices.

Innovation and collaboration: Researchers and developers worldwide contribute to improvements.

Cost-effectiveness: Free to use, reducing upfront costs for businesses and startups.

Customization: Businesses can modify models to fit specific needs.

Rapid advancements: Developers worldwide contribute advancements to speed up progress.

Disadvantages of Open-Source AI

Security risks: Open access can make models vulnerable to misuse or cyber threats.

Complex implementation: Requires in-house expertise for customization and maintenance.

Limited support: Support depends on community contributions.

What Is Proprietary AI?

Proprietary AI refers to AI models, software, and algorithms developed and controlled by organization that restrict access. Using them requires a paid subscription or licensing agreement. These models typically offer stronger security, premium features, dedicated support and regulatory compliance.

Key examples of proprietary AI:

OpenAI’s GPT-4

Google’s Gemini

Anthropic’s Claude AI

Cohere’s Command family

IBM’s watsonx

Advantages of Proprietary AI

Optimized performance: Fine-tuned for efficiency and reliability

Security and compliance: Strong data protection and regulatory compliance

User-friendly integration: Designed for seamless deployment with business applications

Dedicated customer support: Enterprises receive help with implementation and troubleshooting.

Competitive edge: Exclusive AI models can offer a business advantage

Disadvantages of Proprietary AI

High costs: Expensive licensing and usage fees.

Limited transparency: Users have no access to the model’s internal workings.

Vendor lock-in: Dependence on a single provider can be risky.

Slower innovation: Proprietary models rely on internal development teams.

Which Should Businesses Use?

“For most enterprise and other business deployments, it makes sense to initially use proprietary models to learn about AI’s potential and minimize early capital expenditure,” Gadi Singer, director of emergent AI research at Intel Labs, wrote in the aforementioned blog post.

“However, for broad sustained deployment, in many cases companies would use ecosystem-based open-source targeted solutions, which allows for a cost-effective, adaptable strategy that aligns with evolving business needs and industry trends.”

Key factors to consider when choosing between open-source or proprietary AI are the following:

Total Cost of Ownership

Open-source AI models are initially free, making them attractive to startups and organizations experimenting with AI. But their true cost emerges through integration, customization and maintenance.

Open source also needs a technically proficient team to manage configurations, optimize performance and ensure security. It may be costly to hire or train such a team.

However, in the long run, open source tends to be more economical because it avoids recurring licensing fees for businesses that can manage the technical complexity.

Proprietary AI tends to have high initial costs through upfront licensing fees. These fees often include bundled features, support and compliance tools.

However, they offer simplified implementation. Proprietary solutions come pre-configured for specific industries or use cases, reducing the time it takes to deploy. This lowers integration costs and reduces time to market.

Proprietary models do carry recurring license fees, which can increase with scale. But these solutions come with updates, support and maintenance.

There is also the risk of being overdependent on one vendor, which may incur additional costs to migrate to a different solution or when scaling.

Security and Privacy

Open-source AI has more transparency, so global experts can find vulnerabilities and fix them. But it also gives bad actors access to AI models to use for their nefarious activities.

Proprietary AI is more secure, with enterprise-grade protocols that meet compliance standards.

Scalability and Performance

With open source, companies can modify the code and customize it for industry use cases. It offers flexibility in scaling.

With proprietary models, solutions are designed to deliver streamlined performance at scale. But there is no access to its internal workings.