The Slingshot Strategy: Outsmarting Giants with Small AI Models
Introduction In the race to develop increasingly powerful AI agents, it is tempting to believe that size and scale alone will determine success. OpenAI’s GPT, Anthropic’s Claude, and Google’s Gemini are all remarkable examples of cutting-edge large language models (LLMs) capable of handling complex, end-to-end tasks. But behind the marvel lies a critical commercial reality: these models are not free. For enterprise applications, the cost of inference can become a serious bottleneck. As firms aim to deploy AI across workflows, queries, and business logic, every API call adds up. This is where a more deliberate, resourceful approach can offer not just a competitive edge—but a sustainable business model. ...