Open Source vs. Proprietary Models? Why Not Both!

In past articles, we’ve talked about the AI model ownership debate and what business decision makers must bear in mind when choosing between them. And this decision is an inflexion point for any organization that is planning to integrate AI into their operations. 

On the one hand, it will impact costs, security, flexibility, and long-term scalability. On the other hand, a mistake could mean overpaying for AI services, struggling with vendor lock-in, or failing to comply with your industry regulations.   

But what if you don’t have to choose? Every day, more and more organizations are opting for hybrid approaches that combine the best of open source and proprietary models. Now, what do they consist of? What are their pros and cons?  

Let’s find out in today’s article. 

What Even Is Hybrid AI? 

Hybrid AI is the practice of using both proprietary models (like OpenAI’s GPT-4 or Google’s Gemini) and open-source models (like Meta’s LLaMA or Mistral) in one intelligent system or strategy. 

Let’s bring that to life with an example. Suppose a global e-commerce company that is using AI to power both its customer support and internal analytics. 

For its customer service chatbot, it could use a proprietary model like GPT-4, which provides natural-sounding, multi-language support and ensures a reliable user experience—especially during peak traffic periods like Black Friday. 

But behind the scenes, it could be also using a fine-tuned open-source model (such as LLaMA) for internal data processing, like analyzing customer reviews, extracting insights from support tickets, and categorizing product feedback—tailored to their industry jargon and privacy needs. 

The result? A customer-facing experience that’s polished and scalable, paired with an internal system that’s customizable, private, and cost-efficient. 

What Is the Lock-in Risks? 

So, you must bear in mind that there are certain risks in locking into a single AI provider. Let's suppose that your AI strategy depends on a proprietary model like GPT-4 or Claude 3. Now, what happens if the vendor significantly increases API costs? Or if they change the terms of service that limit your use case? And if the model stops being supported or is discontinued?  

All these are actual possibilities. That’s why big companies have started integrating open-source models. For example, Salesforce introduced Agentforce, allowing users to switch between proprietary and open models within their applications. On the other hand, Oracle and SAP now support LLaMA models, letting enterprises integrate both model ownership types.  

Benefits of Hybrid AI Approaches 

So, in short, enterprises want the freedom to switch AI providers and avoid being trapped in long-term dependencies. But the benefits of hybrid approaches can be summarized as it follows: 

• Flexibility: Open-source models give you control. Want to fine-tune your AI to recognize specific industry jargon or behave in a certain way? Go open source. 

• Security & Compliance: Need something rock-solid with clear data governance policies? Use a proprietary model, especially for customer data or regulated industries. 

• Cost Management: Licensing proprietary models 24/7 is expensive. Offload some tasks to open-source models and save big without sacrificing quality. 

• Speed + Customization: Proprietary models can help you go to market faster. Meanwhile, open-source lets your team iterate and innovate freely. 

 A Quick Reality Check: Hybrid Isn’t Perfect 

But let’s not sugarcoat it. Hybrid AI comes with its own challenges: 

• It’s complex. Managing two ecosystems takes time, expertise, and coordination. 

• Security gaps can appear. Especially if you’re using open-source models without proper vetting. 

• Harder to govern. Two models mean twice the compliance checks, documentation, and integration layers. 

But, on top of that, costs can be a major drawback. First of all, "open source” doesn’t mean “free”. In fact, some models charge for access, and even when they don’t, running them requires substantial infrastructure investment.  

Hosting, GPUs, storage, and ongoing maintenance can drive costs sky-high, especially when managing both public and private models. In other words, just like hybrid AI models combines the benefits of open and closed AI, it also brings you the expenses of both ownerships and, for many organizations, they add up fast.  

That’s why the hybrid approach tends to favor larger enterprises with the budget and technical capacity to support it. Smaller companies need to be more strategic—evaluating whether the performance and flexibility gains are worth the financial and operational load that hybrid AI brings. 

Open-Source vs Proprietary Models vs Hybrid AI 

Now, going back to our previous article about AI model ownerships, it’s time to compare the three of them. But, to save you some time, we let you with this infographic for you to decide what is more convenient for your organization: 

Real Talk: What This Means for Business Leaders 

If you're leading a business, your AI decisions aren’t just technical—they're strategic. In this sense, you must consider that the hybrid model can: 

• Future-proof your investments 

• Unlock scalable innovation 

• Balance cost with performance 

• Let you retain control where it matters most 

However, the question now is: “how to know if this is the right approach for my business needs and goals?”. In short terms, hybrid AI models tend to be more profitable and sustainable for: 

• Large enterprises – especially those with high data volumes, complex operations, or strict regulatory needs. They often have the budget, in-house talent, and infrastructure to support both private and open-source models. 

• Heavily regulated industries – like finance, healthcare, and defense, where sensitive data must stay on-premise, but the flexibility of open models is still valuable for innovation. 

• Companies building AI products or platforms – where fine-tuning and control over models is essential, and performance improvements can directly impact revenue. 

But don’t worry. There are other solutions to find the AI model ownership that adjust to your business particularities. And at Inclusion Cloud we’ve both the talents, partners and connections to set the foundations for your AI systems. We help organizations orchestrate that mix—whether it’s integrating open-source models securely or tuning proprietary tools for industry-specific needs.  

Curious how to apply this to your team or workflow? Meet the Inclusion Cloud team in person at Convergence AI in Dallas! As members of the Dallas Regional Chamber, we’ll be sponsoring this event that gathers 600+ industry leaders to check the present and future of AI in business. 

Register in our LinkedIn event and schedule a meeting or join us at the Irving Convention Center this April 30th!