When selecting AI models, developers frequently begin with advanced frontier models like OpenAI’s GPT-4 and Google’s Gemini Ultra. However, these models may become expensive, prompting a shift to smaller open-weight models that offer lower costs—often running at about 20% of the cost while retaining roughly 85% of the accuracy. Forrester analyst Rowan Curran notes that as developers refine their understanding of their applications, they can better optimize costs and model selection.
Thesys CEO Rabi Shanker Guha emphasizes starting with a solid evaluation framework rather than immediately choosing a model. Factors to consider include the data set quality, inference costs, performance, and security features. As new models emerge, flexibility is crucial; Vercel’s AI SDK allows easy model switching, ensuring efficiency and adaptability in development. Companies like SAP are also enhancing developer experiences by providing abstraction layers to facilitate model transitions, maintaining security and performance standards across varying applications.
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