Open source vs commercial AI: choosing the right path for your business

This blog is based on a presentation by Guillaume, Field Chief Technology Officer at Upsun, and Robert from Ilwiin Technology during the AI Action Summit. The original French presentation has been translated and edited for clarity and accuracy.

The AI field is advancing significantly, presenting organizations with the question: Should they choose open-source or commercial AI models? This choice impacts everything from costs and data privacy to long-term business strategy. Let's explore the key differences and help you make an informed decision.

The current AI ecosystem

There are two main types of AI models: open-source models, which are freely available and accompanied by public code, and proprietary models, which are licensed and typically come with restricted access to code. Secondly, commercial models, which are paid services from tech companies like OpenAI, that you access through subscriptions.

The gap between these two approaches is narrowing. Open-source models now achieve performance levels comparable to those of commercial models, typically with a delay of about 6 months. Recent examples, such as Chinese models, have even matched the performance of leading commercial solutions.

Open source AI: advantages and considerations

Key Benefits

• Cost control: Open source models eliminate subscription fees and reduce long-term costs. While you still need computing resources to run them, you avoid the marketing and acquisition costs built into commercial pricing.
• Data privacy: Your data stays under your control. This is crucial for organizations that handle sensitive information or operate in regulated industries.
• No vendor lock-in: You can switch between different models or providers without being tied to a single company's ecosystem.
• Transparency: You can examine the model's behavior and understand how it processes information, though training data is often not fully disclosed.

Challenges to consider

• Technical complexity: Running open source models requires finding suitable hosting providers with GPU capabilities. It's not as simple as signing up for a service.
• Resource requirements: You need technical expertise to deploy, maintain, and optimize these models effectively.
• Infrastructure costs: Although the models are free, you still incur costs for computing power and storage.

When commercial AI works better

Advantages

• Ease of Use: Commercial solutions offer plug-and-play simplicity. You can start using advanced AI capabilities immediately.
• Professional Support: Companies provide customer service, regular updates, and technical assistance.
• Reliability: Established providers offer stable, tested solutions with guaranteed uptime.

Drawbacks

• Rising Costs: Current pricing is heavily subsidized. As AI becomes more mainstream, costs will likely increase significantly.
• Data Concerns: Your information passes through third-party systems, raising concerns about privacy and security.
• Limited Control: You depend on the provider's decisions about features, pricing, and availability.

The rise of small language models

A significant trend, as discussed by Guillaume, is the use of  Small Language Models (SLMs). These focused models perform specific tasks just as well as large general models but use far fewer resources. For functions such as document summarization, customer service, or content classification, small models yield excellent results at significantly lower costs.

As Guillaume noted, "we're moving toward more frugal models" that are "extremely efficient." This isn't trendy, but it's practical; many production systems in companies work better with smaller, specialized models than with massive general-purpose ones.

Making the right choice for your organization

During the session, the speaker emphasized that successful AI implementations utilize multiple models working in tandem. This "agentic approach" breaks down complex problems into simpler components, with different AI models handling distinct parts.

Companies are building systems that utilize vision models to analyze images, language models for text, and specialized models for specific tasks, all orchestrated together. This requires engineering tools to manage these complex workflows.

Choose open source if:

• Data privacy is critical for your business
• You have technical expertise in-house
• Cost control is a priority
• You want to avoid vendor dependence
• You need customization for specific use cases

Choose commercial solutions if:

• You need immediate deployment
• Technical resources are limited
• You prefer predictable monthly costs
• Professional support is important
• You're testing AI capabilities before a significant investment

Recommendation

During the session, the speaker emphasized that companies that succeed with AI focus on specific business problems rather than simply adopting AI in general. They start with use cases that are "at the heart of the business model" rather than trying to use AI everywhere. The most important factor is avoiding both technological and financial vendor lock-in. As AI technology advances rapidly, flexibility becomes increasingly valuable, as committing to a single approach becomes less viable.

Cost considerations

Current AI pricing from major providers includes significant marketing and customer acquisition costs. As these companies mature and competition intensifies, pricing structures are likely to change. Organizations should plan for:

• Potential price increases as subsidies end
• Volume-based pricing for heavy usage
• Different pricing tiers based on performance requirements

Whether you choose open source or commercial AI, success depends on:

1. Clear Use Cases: Focus on specific business problems rather than general AI adoption.
2. Pilot Projects: Start small with well-defined objectives.
3. Team Training: Ensure your team understands both the capabilities and limitations of AI.
4. Regular Evaluation: Monitor performance, costs, and changing requirements to ensure ongoing alignment.

Conclusion

The choice between open source and commercial AI is not permanent. The most successful organizations maintain flexibility, regularly evaluate their options, and choose the right tool for each specific use case.

Open source AI offers cost savings, privacy control, and customization options, but requires technical expertise. Commercial AI offers convenience and support, albeit at higher costs and with reduced power. As the AI ecosystem continues to rapidly evolve, staying informed about new developments and maintaining a flexible approach will serve your organization better than locking into any single solution too early.

The future belongs to organizations that can effectively combine different AI approaches, choosing the right model for each task while maintaining control over their data, costs, and strategic direction.