The article discusses the practical experience of transitioning from local language models to APIs for business applications. The author, a bootstrapped founder of Podscan, initially favored local models for cost savings and control. However, real-world experiences revealed that using APIs was often more efficient due to the scale and cost advantages of providers like OpenAI and Anthropic. The author identifies scenarios where local models can be beneficial, specifically for small, quick decision-making tasks, emphasizing the importance of context and the practical aspects of deployment choices.
When I first started building Podscan, I was convinced I had to do everything with local language models.
The cost savings that platforms like OpenAI and Anthropic have achieved... made it pretty clear that, for my workload and data volume, it made no sense to rent more and more GPU resources.
The first sweet spot is when you have very small tasks that need quick decisions.
Instead of making a network call and dealing with API latency, you can get your answer instantly.
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