The Pros and Cons of Open & Closed-Source Infrastructure for GenAI Apps

Timescale’s Avthar Sewrathan offers insight on the pros and cons of open and closed-source infrastructure for GenAI apps. This article originally appeared on Solutions Review’s Insight Jam, an enterprise IT community enabling the human conversation on AI.

AI is eating the world – every business today is either looking to integrate the newfound capabilities of large language models (LLMs) into their existing applications or build entirely new applications with AI at the center to better serve their users. But with this push for innovation comes a choice: Should engineering teams use open-source or proprietary infrastructure as the foundation for their GenAI applications? 

Like most engineering problems, the decision is not black or white. It’s about selecting the right tool for the specific needs of your application, and understanding the trade-offs involved.

One way to do this is to consider what you need your system to do and then evaluate what issues could stop you from meeting those goals. Let’s use these potential obstacles to compare open-source and closed-source models and databases to determine which one is the better solution for you.

Obstacle 1: Data Privacy Concerns

At the heart of useful AI applications is connecting LLMs with your private, proprietary, or customer data. And you don’t need to run a survey to know that data privacy concerns are top of mind for users. So the question is which solution will give you better privacy guarantees for your AI application?

Closed-source AI models present significant challenges in terms of data usage transparency and accountability. While providers of these models may claim they won’t monetize personal data or use it for model training, the opacity of their systems makes independent verification impossible. Furthermore, the terms of service for such platforms are subject to unilateral changes, potentially leaving clients in precarious positions regarding data governance.

Open-source LLMs, in contrast, offer unprecedented control over data utilization and model deployment. By self-hosting these models, organizations can implement robust privacy-preserving measures at every stage of the AI pipeline. This approach guarantees that sensitive data never leaves the organization’s secure infrastructure during model inference or fine-tuning processes. Open-source LLMs enable the implementation of advanced techniques like local differential privacy or federated fine-tuning, ensuring that even aggregated insights derived from private data remain protected. Additionally, the transparent nature of open-source model architectures allows for comprehensive security audits, enabling organizations to verify that no backdoors or unintended data leakage points exist within the model itself. This level of scrutiny and control is simply not possible with closed-source alternatives, where the internal workings of the model remain opaque. Industry regulations also play a part. For those who work in highly regulated environments, such as finance and healthcare, open-source models may provide far lower risk than closed source.

This control also extends to open-source databases like PostgreSQL, which offer full visibility into data storage and management processes. PostgreSQL provides robust security features including AES-256 encryption for data at rest, SSL/TLS for data in transit, and granular role-based access controls. Its extensible architecture allows for custom security modules, while built-in audit logging capabilities aid in regulatory compliance. As an open-source system, PostgreSQL’s codebase can be thoroughly audited, ensuring no hidden vulnerabilities exist. This level of transparency and control enables organizations to implement tailored data protection measures, crucial when handling sensitive information for AI applications.

Obstacle 2: High Costs

Nothing can stifle innovation and growth faster than running out of cash or resources. So, which paradigm gives you the best chance at controlling costs?

Closed-source models offer rapid deployment via API integration, using pay-per-token pricing. While these models excel at complex reasoning tasks, they carry risks of future price or feature changes. In general, the best closed source models are more capable than the best open-source models, but they are also more expensive on a per token basis.

Open-source models, on the other hand, require more initial setup time but provide long-term cost control and predictability. Costs are primarily driven by hardware and operations, and there’s competition between GPU makers and providers to keep prices competitive. 

Rather than using a single model for all tasks in your application, it helps to select models on a per-task basis, matching the model capabilities and cost to the complexity of the task at hand. For example, I see many customers at Timescale using proprietary models like GPT4o or Claude Sonnet for complex reasoning and synthesis tasks, but also using open-source models like Mistral or Llama 3 for simpler tasks like summarization, classification or data formatting.

Likewise, open-source databases can help you control costs in the long term, as you can optimize them to scale based on your app’s specific data needs.

Obstacle 3: Version Control and Model Stability

Version control in AI models is crucial for maintaining consistent performance and reliability in applications. This issue is particularly pertinent with closed-source models, where updates can unexpectedly alter model behavior or capabilities.

Closed-source models, while often state-of-the-art, present unique challenges in version control. Users have limited control over update timing and content, which can lead to sudden changes in model performance. For instance, there have been documented cases of major AI labs releasing updates that inadvertently decreased model capabilities or introduced new biases. This lack of control can be particularly problematic for production systems that rely on consistent model behavior.

Open-source models, conversely, offer greater flexibility in version management. Developers can:

1. Pin their applications to specific model versions, ensuring stability.
2. Thoroughly test new versions before deployment.
3. Roll back to previous versions if issues are discovered.
4. Contribute fixes or customizations directly to the model codebase.

This level of control extends to the entire AI stack, including databases. With open-source databases like PostgreSQL, teams can manage updates on their own schedule, implement custom patches, and maintain compatibility with their specific AI workflows.

Obstacle 4: Long-term Viability and Data Sovereignty

The sustainability and continuity of AI models are critical concerns for production applications. Closed-source models, while powerful, introduce significant risks:

1. Business continuity: Sudden corporate changes can jeopardize access to critical AI infrastructure.
2. Feature volatility: API providers may alter or remove crucial functionalities with limited notice.
3. Licensing uncertainty: Shifts in pricing or usage terms can impact application economics.
4. Acquisition risks: Corporate takeovers may lead to strategic pivots, potentially abandoning existing users.

Open-source models mitigate these risks by providing code persistence, forking capability, community support, and customization freedom. This approach extends to the entire AI stack, including databases like PostgreSQL, offering data portability, schema evolution control, and custom extension capabilities.

Technically, this enables:

• Reproducible AI pipelines through version-controlled models and data infrastructure.
• Comprehensive audit trails for security and performance.
• Flexible hybrid deployments, optimizing for performance, cost, and control.

While closed-source models offer cutting-edge performance, open-source alternatives provide a foundation for sustainable, controllable AI systems. This ensures core technologies remain accessible and adaptable, regardless of market dynamics or corporate decisions.

Conclusion

The choice between open-source and closed-source AI infrastructure isn’t binary—it’s about finding the right balance for your specific needs. Closed-source models offer cutting-edge performance and easy deployment, ideal for rapid prototyping and complex tasks. However, they raise concerns about data privacy, cost predictability, and long-term control.

Open-source alternatives provide greater control, transparency, and customization, excelling in data privacy, cost management at scale, and long-term sustainability. They require more initial setup but offer unparalleled flexibility.

Many organizations are adopting a hybrid approach, using closed-source models for complex reasoning and open-source options for routine tasks. This strategy balances performance with cost efficiency and control.

The key is to align your infrastructure choices with your business objectives, regulatory requirements, and long-term goals. By understanding the trade-offs and staying informed about both open and closed-source developments, you can build AI applications that are powerful, secure, cost-effective, and adaptable to the rapidly evolving AI landscape.