Proprietary vs. Open-Source LLMs: Best Choice for Enterprises

Enterprise LLMs: Why Use a Proprietary LLM Over Open-Source LLM for Deploying Gen AI? 

As businesses dive into the world of generative artificial intelligence (Gen AI), Large Language Models (LLMs) play a pivotal role in steering innovation and operational transformations. The decision for enterprises to adopt a proprietary (closed-source) or open-source LLM is more than just a technological choice–it’s a strategic one. Proprietary LLMs offer a robust foundation for businesses eager to develop, test, and deploy Gen AI applications swiftly and securely.  

 At Encora, we advocate for proprietary LLMs as the most straightforward approach for enterprises, providing built-in measures against biases, misuse, and other common issues. This discussion sheds light on why proprietary LLMs stand out as the preferred option for enterprises venturing into Gen AI.  

What is an open-source LLM? 

Open-source LLMs are developed in a transparent, collaborative environment where the source code and weights are publicly available and free for anyone to use, modify, and enhance. This openness fosters innovation and allows for rapid evolution, as developers from around the globe can contribute to the model’s growth and improvement.  

Open-source LLMs, such as those offered by academic institutions and collaborative projects, embody the spirit of communal advancement in artificial intelligence. They enable businesses to explore Gen AI capabilities without the upfront cost of proprietary systems. However, while open-source LLMs promise flexibility and accessibility, they may require significant in-house expertise to customize and maintain, presenting challenges in scalability, security, and support for enterprises.  

What is a proprietary LLM? 

Proprietary LLMs are developed and managed by individual companies, with their source code, training processes, and data kept confidential. These models, such as those created by Azure, AWS, and GCP, are designed with enterprise needs in mind, providing tailored solutions that align with business objectives.  

Proprietary LLMs are characterized by their robust security measures, scalability, and dedicated support, ensuring that businesses can deploy Gen AI applications with confidence. By opting for a proprietary model, enterprises benefit from continuous updates, maintenance, and assurance that the model adheres to the highest data privacy and compliance standards. Companies creating proprietary models focus on guaranteeing safe and non-biased models by further tuning the models over time. Furthermore, proprietary models are often accompanied by enterprise-grade offerings that enable additional layers of protection and privacy, such as those to prevent leakage of propriatry data. These enhanced, enterprise-grade security features provide a crucial peace of mind for organizations. The trade-off, however, is a dependence on the provider for updates and potentially higher costs compared to open-source alternatives.  

Open Source vs Proprietary LLM for Enterprises 

Pros and Cons of Open Source LLMs 

 Pros:  

• Innovation and Flexibility: Open-source LLMs encourage innovation, allowing enterprises to experiment and adapt the technology to their specific needs.  
• Transparency: Through the open-source model, enterprises can seek to fully understand, customize, and audit the model’s behavior.  
• Community Support: A vast community of developers contributes to improving and troubleshooting open-source LLMs.  
• On-Prem Compatible: If a company wants to transition to an on-premise model and deploy locally as opposed to on the cloud, they can do so with open-source LLMs. NVIDIA supports the tech stack required for cloud repatriation, allowing companies to invest in the necessary GPU-infrastructure.  

Cons:  

• Security and Privacy Concerns: Open-source models may not meet the privacy and security requirements of enterprises. 
• Maintenance and Support: Enterprises must rely on their in-house capabilities or seek external expertise to maintain and update open-source LLMs.  
• Not Enterprise-Ready: Open-source LLMs are not designed for enterprises, so they would require a significant amount of training and customization before they could be deployed for most enterprise-level use cases, particularly those that are customer-facing. Upgrading an open-source LLM for enterprise use is typically not feasible for enterprises to handle on their own as doing so requires significant resources to train, launch, and maintain.  

Pros and Cons of Proprietary LLMs 

Pros:  

• Tailored for Enterprises: Proprietary LLMs are designed with business needs in mind, offering solutions that are scalable, secure, and compliant.  
• Ready-to-Deploy Solutions: Backed by their owning entity’s investment in research, development, and refinement, proprietary LLMs offer ready-to-deploy solutions designed specifically for commercial use.  
• Dedicated Commercial Support: Providers offer ongoing support, updates, and maintenance, ensuring the LLM evolves with the business.  
• Competitive Advantage: Unique capabilities and features give enterprises a competitive advantage over those using open-source LLMs that are accessible to the general public. Furthermore, working with an engineering partner, like Encora, enables enterprises to customize the LLM for their unique needs to yield a competitive advantage.  

 Cons: 

• Cost: Proprietary LLMs often come with higher initial and ongoing costs than open-source models.  
• Vendor Lock-in: Reliance on a single provider could limit flexibility and control over future technological decisions.  

Choosing Between Open Source vs Closed Source LLM for Your Enterprise  

Choosing the right LLM for your enterprise is a critical decision that can shape your technological landscape for years to come. Deciding between an open-source and proprietary (or closed-source) LLM hinges on several factors, including compliance requirements, security expectations, support needs, and the desire for a rapid time to market. While open-source LLMs may be appealing for their accessibility and cost-savings upfront, optimizing them for enterprise use typically demands robust in-house IT teams experienced in Gen AI–a requirement beyond the reach of most enterprises today.  

For the majority of enterprises, proprietary LLMs provide a secure, scalable, and supportive framework that is often better suited to the intricate needs of enterprises. These models ensure enterprise-grade compliance and security and provide distinct capabilities and dedicated support for the successful deployment and ongoing use of Gen AI applications.  

Leveraging Proprietary LLM Solutions for Enterprises with Encora   

Encora partners with industry leaders Azure, AWS, and GCP to guide enterprises through the complexities of Gen AI, ensuring they select the best LLM model to meet their specific business challenges and objectives. Our expertise in understanding the nuanced needs of each client, coupled with our strategic partnerships, enables us to map those needs to the strengths of proprietary LLM solutions.   

By collaborating with Encora, enterprises gain access to modern technology, expert guidance, and the capability to rapidly deploy Gen AI applications that drive tangible business value. Our role is to demystify the process of choosing and implementing an LLM, making proprietary solutions accessible and a powerful tool for businesses seeking a competitive edge in their industry.