Key Takeaways
- Open vs. closed AI trade-offs: Open models offer transparency and flexibility, while closed models provide advanced features but limit control.
- Vendor lock-in risks: Proprietary AI can lead to costly dependence on a single provider, impacting adaptability and long-term strategy.
- Transparency & security: Open-source AI allows full auditing, customization, and better control over data security and compliance.
- Hybrid approach: Combining open and closed AI models balances flexibility, performance, and risk mitigation.
Listen: What's better? Open-source versus closed-source AI.
The rapid advancement of artificial intelligence (AI) is reshaping industries around the world. For many executives and decision-makers, integrating AI into business operations is a competitive necessity. However, selecting and integrating the right AI models is a critical decision that often receives less attention than it deserves. Licensing terms and whether a model is open or closed source can significantly impact your organization’s technological capabilities, operational risks, and long-term strategic flexibility. How does your organization evaluate the best AI model for your needs? Let’s discuss the key factors to consider.
What is an AI model, and why does it matter?
At its core, an AI model is a computer program designed to excel at specific tasks that traditionally require human intelligence—such as reasoning, problem-solving, or understanding natural language. Unlike conventional software, AI models are built on statistical methods and complex learning routines that analyze vast datasets to recognize patterns and make predictions. This unique nature makes AI models both powerful and, at times, difficult to fully understand even by their creators.
Understanding AI model licensing
AI model licensing ranges from completely free public domain releases to strictly controlled proprietary systems. Some models are open source, meaning anyone can download, inspect, and even modify the underlying code and parameters. This transparency encourages community development, rapid improvement, and thorough peer review. Organizations can adapt open-source models to meet specific needs and maintain full control over the model’s availability, tune it to specific use cases, and maintain tight control of the data that’s sent to and from the model.
Conversely, closed-source models are proprietary and operate in a more guarded framework. Their source code, internal parameters, and even the training data are kept confidential. Instead of direct access, developers interact with these models via service endpoints provided by the vendor. This approach ensures that the inner workings of the model remain a trade secret, which helps protect the vendor’s competitive advantage. However, the trade-off is a significant loss of transparency. Users are often unable to verify how decisions are made by the model or customize it to better fit their unique requirements. This can pose challenges in terms of compliance and data security, especially for industries that must adhere to strict regulatory standards. While proprietary models typically come with dedicated support and regular updates, they can lock organizations into a specific vendor ecosystem, limiting flexibility and long-term strategic choices.
The high cost of building AI models
Training an AI model is an exceptionally complex and resource-intensive process. Consider that one version of Meta’s Llama 3 model required nearly 39.3 million training hours distributed over thousands of computers. This staggering amount of computing power reflects not only the technical sophistication needed to build a high-performing model but also the significant investments in advanced hardware, energy, and specialized talent. Every stage of the process—from gathering and cleaning massive datasets to designing intricate algorithms and fine-tuning model parameters—adds layers of cost and complexity.
Given these immense costs, it becomes clear why companies are motivated to keep their models proprietary. The financial, technical, and intellectual resources poured into developing an AI model create a substantial competitive advantage. By maintaining secrecy around their proprietary models, companies can protect their investments and prevent competitors from easily replicating or reverse-engineering their innovations. This protective approach not only helps secure a unique market edge but also enables companies to offer specialized services, robust support, and continuous updates driven by ongoing research and development. In essence, the high cost of building these models naturally leads companies to safeguard their intellectual property, ensuring long-term strategic benefits and market leadership.
Risks of relying on closed-source models
Using closed-source models can create a significant dependency on a single vendor, which limits your flexibility to adapt to market changes. When your entire AI strategy relies on a single vendor like OpenAI—well-known for models such as GPT-4—your organization’s operational future relies on that vendor. If the vendor decides to adjust their pricing structure, the financial implications for your organization can be substantial. An unexpected price hike could force you to reallocate resources, limit your usage, or discontinue usage, having a devastating impact on your business.
Changes in a vendor’s service offerings can also pose serious risks. For example, if the vendor decides to discontinue a particular model or change its feature set, your systems may no longer be compatible with their offerings. Transitioning to an alternative provider under these circumstances may prove incredibly difficult, depending on the depth of integration and level of reliance it has on that particular vendor.
The restrictions inherent in closed-source models extend beyond pricing and service continuity. They can also limit your ability to customize and fine-tune the model to better suit your specific needs. This inflexibility can hinder rapid adaptation to market changes, especially in fast-evolving industries where agility is key. Additionally, the opaque nature of closed-source models can also complicate compliance efforts and risk management. Without full transparency into how the model operates, it becomes challenging to ensure that your system meets regulatory standards or to diagnose issues when they arise.
Finally, another layer of risk comes from proprietary data security and regulatory compliance. When you send sensitive or proprietary data to a vendor’s service, you’re not only relying on the vendor's security measures but also on their commitment to complying with industry regulations. Any changes in their security protocols or a breach in their system could expose your data to significant risk, leading to potential legal and financial repercussions.
Advantages of open source models
Open-source models have matured into robust alternatives that rival proprietary systems in both performance and flexibility. Because open-source models are freely available, they foster a competitive ecosystem where multiple vendors can offer hosted solutions. This competition not only drives down operational costs but also provides organizations with a variety of deployment options tailored to their specific needs. Organizations can choose from various service providers, ensuring they get the best balance between performance, price, and support.
Another significant advantage is the ability to host these models in-house. For businesses dealing with sensitive or regulated data, keeping data internal may be a requirement. Hosting in-house means you have complete control over the data and the environment, which is crucial for meeting strict regulatory requirements and maintaining control over proprietary data.
The open nature of these models means that every line of code and every decision-making process can be scrutinized by both internal teams and the broader community. This level of transparency not only enables deep audits that verify the model's logic and reasoning but also helps identify biases, security vulnerabilities, and compliance issues before they become critical problems. By allowing stakeholders to thoroughly inspect and understand how decisions are made, businesses can more confidently implement AI solutions, knowing that they can mitigate risks associated with opaque decision-making processes and maintain higher standards of accountability and safety.
Balancing the trade-offs
The decision between open and closed source isn’t always clear-cut. Closed-source models may offer superior performance or cutting-edge features due to the significant investment behind their development. Their proprietary nature can also provide a competitive edge. On the other hand, open-source models offer greater transparency, flexibility, and cost control. Smaller organizations might benefit from the structured support and regular updates that often come with proprietary models, while larger organizations might value the ability to host and tailor open-source solutions in-house.
A hybrid strategy that blends open- and closed-source models allows organizations to harness the strengths of both approaches while mitigating their inherent weaknesses. By leveraging open-source models, companies benefit from transparency, community-driven improvements, and the flexibility to tailor systems to meet specific regulatory or security requirements. At the same time, integrating closed-source models can provide access to cutting-edge performance, specialized features, and structured vendor support that may be critical for high-stakes applications. This balanced approach not only diversifies technological reliance—reducing risks associated with vendor lock-in and pricing changes—but also enables businesses to optimize their AI systems for both efficiency and innovation.
Making the right choice for your organization
When deciding which AI model to integrate into your system, consider these questions:
- What are the total operational costs associated with each model?
- How important is transparency and auditability in your industry?
- Does your organization have the expertise to manage self-hosted models, or would a vendor-hosted solution be more suitable?
- What level of risk can you tolerate regarding vendor pricing changes and dependency?
For medium to large organizations, aligning licensing decisions with operational scale and regulatory requirements is crucial. Balancing risk—especially concerning data security and vendor dependency—with performance and feature requirements is key to making an informed decision.
Choosing between open- and closed-source AI models is a strategic decision that will affect your organization’s technological agility, risk management, and overall costs. By carefully weighing the trade-offs between transparency, flexibility, performance, and support, you can select an AI solution that not only meets today’s demands but also positions your organization for long-term success.
About the authors
Brian is a results-driven business executive with a proven track record of over 20 years, specializing in marketing operational excellence within the Technology, Media, and Entertainment industries. He leads a team that collaborates with top-tier brands, driving growth and streamlining marketing operations for increased efficiency and profitability. A trusted advisor to his clients, Brian provides strategic guidance on navigating the complexities of the modern marketing world. His commitment to excellence is evident in his academic achievements: Bachelor of Arts in Communication from the University of Colorado and is a Certified Contingent Workforce Professional. He currently serves as a program advisor for the Strategic AI program at the University of Colorado Colorado Springs.
Josh is the cofounder and engineering force behind Cardsetter, a platform that helps small businesses get online. A U.S. Navy veteran and a graduate of the University of California, San Diego, he built his first website in 1997 over a dial-up modem. That early curiosity became a lifelong passion for web technology. Today, he creates smarter, more accessible web solutions and writes about AI and emerging technologies, making complex topics easy to understand and apply.
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