Comparative Analysis of the Pros and Cons of Open-source, Permissive and Proprietary Foundation Models

Introduction

In the dynamic world of AI, a central debate considers the risks and benefits of Open source vs Proprietary Foundation Models that form the backbone of many AI applications. The decision revolves primarily around open-source models, with their emphasis on transparency and community-driven advancement, and proprietary models, driven by cutting-edge research teams, performance and commercial focus. Also, with the emergence of permissive models that are proprietary but provide permissive licenses, the solidifying the parameters of risk to build sound enterprise grade LLM-based applications remains a balancing act which takes into vital consideration the pros an cons of each type of model.

In this article we explore aspects of risk and benefit that can aid decision-makers as the landscape of generative AI-based production system development continues to evolve at a breathless pace.

We attempt to venture beyond pros and cons, offering a nuanced technical exploration designed to enlighten stakeholders facing this complex choice. We will dissect the distinct characteristics, inherent potential, and practical limitations of each model type in the context of LLMs.

Lastly, we will explore the needs of production grade generative AI beyond just the foundation and customized models.

Note: Domain-specific models that are trained on an organizations own data or from a specific industry vertical like MedLM or SecLM are tuned models that leverage the foundation models cited here and specialize them for domain specific applications.

Pros and cons of Open Source, Permissive and Proprietary Foundation Models (image generated by author)

Open-Source Foundation Models: The Promise and Challenges of Democratized AI

The principle of open-source, deeply ingrained in software development, has made its way to AI. Its primary allure lies in transparency and a thriving collaborative environment.

· Transparency and Auditability: Unlike their proprietary counterparts, the very blueprints of open-source models are open to scrutiny. This enables critical oversight of their internal workings, exposing potential biases that can be actively addressed. Transparency is integral to advancing truly ethical AI systems.

· Customization: In open-source, customization isn’t merely allowed, it’s actively encouraged. Developers can fine-tune models, honing them for specific datasets and tasks. This customization facilitates domain-specific adaptations that drive tailored performance improvements.

· Cost-Effectiveness: Absent of licensing fees, open-source models can provide significant financial advantages, especially for long-term deployment or when factoring in potential large-scale customization.

· Community Support: Harnessing the wisdom of the crowd, a broad community fuels innovation. Bug fixes, security improvements, and even conceptual upgrades may benefit from collective expertise, ensuring a robust development life cycle.

· Enhanced Data Governance: With open-source models, users maintain sovereignty over their data. This control extends privacy and security by reducing reliance on external data management practices found in proprietary systems.

However, the open-source path is not without its challenges:

· Technical Expertise Required: Effective adoption hinges on significant technical proficiency. Modifying models and tailoring their operation require specialized knowledge, a potential barrier for organizations lacking dedicated AI teams.

· Limited Formal Support: Users rely heavily on peer-to-peer aid and community-driven knowledge bases. While vibrant, this support structure may not match the swiftness and specificity of a company’s professional support channels.

· Consensus-Driven Development: Changes are subject to community approvals, which can sometimes slow innovation timelines compared to proprietary models backed by centralized authority.

· Security Risks: While the adage “many eyes make bugs shallow” holds some truth, community security management may take time to catch up to the rigorous, dedicated efforts found in commercially developed models.

· Performance Compromises: The trade-off between accessibility and absolute optimization frequently surfaces. Open-source models prioritize flexibility and adaptability, which may entail subtle sacrifices in performance when compared to proprietary peers.

Proprietary Models: Pioneering Excellence with Exclusive Technology

Backed by the computational horsepower and vast data stores, resources and research teams of tech giants, proprietary models strive to maintain their lead in AI performance.

· State-of-the-Art Performance: Vast resources in the form of compute power and extensive datasets underpin breakthroughs in these models. When pushing the boundaries of AI capability is paramount, proprietary models often stand alone.

· User-Friendly Design: Ease of adoption is a cornerstone. Seamless APIs and well-documented integration steps widen accessibility to non-specialists seeking quick entry into AI.

· Dedicated Support: Companies offer highly responsive support teams focused on resolving issues, guiding use cases, and even facilitating bespoke custom modifications.

· Continuous Improvement: Frequent updates fueled by focused development teams keep models competitive with constant refinements, new features, and enhanced performance.

· Seamless Integration: Proprietary models typically offer tight integration within a developer’s existing toolkit or product ecosystem, minimizing development friction.

Naturally, this power and refinement come with considerations:

· Higher Costs: Licensing and usage-based fees quickly accumulate, especially for extensive or large-scale projects. Cost factors can present considerable constraints.

· Limited Customizability: To protect intellectual property, users may encounter barriers to adapting the model’s core components or gaining insight into its decision-making algorithms.

· Possibility of Model Lock-in: Deep integration with a particular proprietary suite can result in substantial financial and technical hurdles for potential migration to competing or open-source alternatives.

Privacy and Ethical Concerns: Opaque “black box” models raise questions about the uses of user data and how AI’s decisions are reached, highlighting the need for ethical AI governance.

High Risks of Permissive Models

Permissive models, like Meta’s Llama 2, represent a middle ground between open-source and proprietary large language models, aiming to harness the strengths of both. However, this approach introduces specific risks that stakeholders must carefully consider:

- Licensing Ambiguity: One of the primary challenges with permissive models is the complexity and variability of their licensing terms. These terms can significantly restrict how the models can be used, especially in commercial settings. The ambiguity around what is and isn’t allowed can lead to legal and operational risks for organizations that fail to comply with these terms.

- Inconsistent Support and Updates: Unlike proprietary models, which benefit from dedicated development and support teams, permissive models may suffer from inconsistent updates and support. This can lead to situations where bugs or security vulnerabilities are not addressed in a timely manner, potentially compromising the integrity and performance of applications built on these models.

- Performance Uncertainty: Permissive models may not always match the performance and efficiency of fully proprietary models due to resource constraints or design compromises. This performance gap can be critical for applications requiring the utmost accuracy and responsiveness, leading to potential competitive disadvantages.

• Bias and Ethical Concerns: While permissive models aim to balance transparency with control, they still inherit biases present in their training data or methodologies. Without comprehensive transparency, it can be challenging for users to identify, understand, and mitigate these biases, raising ethical concerns and risking public trust.

Risks and Benefits of AI Foundation Model Types

Beyond Models: Building Secure, Private, and Responsible AI Applications with LLMs

Large language models like Google Gemini and OpenAI GPT demonstrate captivating potential, yet real-world business solutions depend on more than model choice. Responsibility, reliability, and tangible value sit atop concerns of any production-grade AI deployment. We explore the cornerstone characteristics that transform experimental capabilities into secure, private, and ultimately beneficial AI applications.

1. Data Foundations for Bias Mitigation & Fair Outcomes

• Data Representativeness: LLMs learn from the data they are fed. Unrepresentative or skewed datasets are ‘baked in’, manifesting in biased responses. Careful curation, continuous analysis, and a variety of de-biasing techniques are needed for fair and inclusive applications.
• Explainability of Outcomes: The ability to understand and explain why the LLM outputs a particular response is central to mitigating potential bias and enhancing the credibility of decisions influenced by the AI. This level of understanding goes beyond basic input-output analysis.
• Regular Auditing and Evaluation: Constant vigilance is vital. Biases can shift as language usage evolves, necessitating regular data and outcome audits using metrics designed to flag discrimination and unfairness — not just accuracy.

2. Privacy and Security: Beyond a Bullet Point

• Robust Data Handling: Strict access controls, secure storage practices, and encryption must be integrated throughout the application workflow. This encompasses model inputs, outputs, and any user data collected for evaluation or personalization.
• Transparency: Clearly articulate how data is used within the application. Provide meaningful choices to users over how their data is collected and leveraged, empowering them to make informed decisions that preserve their privacy.
• Security by Design: Security breaches undermine any benefits LLMs provide. Rigorous testing practices, code vulnerability analysis, and a defense-in-depth approach must be part of the development process from day one.

3. From Accuracy to Real-World Reliability

• Beyond Benchmark Performance: Datasets like those used to train and test LLMs may not replicate the complexities of your application’s specific use case. Build extensive unit and integration test suites tailored to your unique needs.
• Graceful Failure: No AI is perfect. Design your application to fail gracefully under unexpected or unusual inputs. Provide ways to capture user feedback and have clear escalation mechanisms for complex issues.
• Measurable Outcome Metrics: Establish KPIs beyond raw model accuracy. Consider the real-world benefits of automation, faster decisions, or enhanced customer service that the application should yield. Track these and iterate.

4. Human-in-the-loop: AI + Expertise

• Responsible Oversight: In mission-critical scenarios, always ensure humans review decisions influenced by LLM output. Particularly true for cases where outcomes impact safety, legal rights, or significant financial burdens.
• Collaborative Interface: Effective tools for real-time oversight are key. The system should clearly explain the AI’s suggested response along with its confidence, along with options for human intervention or alternative action.
• Continuous Learning: A symbiotic relationship between the AI and human users enhances outcomes over time. Design mechanisms for capturing user feedback and retraining the model in-place to refine its accuracy and usefulness.

5. Return on Investment — The Bottom Line

• Value Proposition: Clearly define how the LLM-based application creates business value. This could be efficiency gains, error reduction, or even the unlocking of entirely new products and services previously inconceivable.
• Operational Cost vs. Benefit: Factor in the ongoing costs of model retraining, data upkeep, security investments, and human resources allocated to oversight when calculating the total cost of ownership.
• Iterative Approach: It’s rare that AI is a “deploy and forget” technology. Expect ongoing refinement. Set realistic expectations upfront with stakeholders and build adaptability into your budget and deployment plans.

Conclusion

The power of LLMs presents transformative opportunities, but only when harnessed responsibly. Privacy, bias mitigation, reliability, and an emphasis on quantifiable impact form the building blocks for AI applications that not only impress but genuinely contribute to your business objectives. It is key to look beyond just models and look for solution completeness. See this blog post for more discussion on solution completeness.