How to choose an AI model: lessons from Notion and Gamma

In the evolving landscape of AI model selection, companies are re-evaluating their strategies to optimize for cost, reliability, and task-specific performance rather than defaulting to the most powerful models. Harnesses, which are essential for making large language models (LLMs) functional, must be tailored to each model's unique training structure to avoid degrading performance. The flexibility to switch models is becoming increasingly valuable as it allows businesses to adapt quickly to new offerings, manage costs, and maintain reliability. Open-weight models are catching up with closed models, offering more customization options and enabling access to markets with lower price sensitivity. Fine-tuning and reinforcement learning (RL) provide avenues for growth beyond just improving profit margins, allowing for more accessible AI-enabled work by lowering costs. As companies like Gamma and Notion explore further advancements such as image generation and data governance systems, the emphasis remains on using the right model for the right task, evaluating both the model and the provider to maintain control over pricing and quality.

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