390: When to Choose Local LLMs vs APIs

May 16, 2025 16m

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Episode Description

When I started Podscan, I wanted to go full local AI. Self-hosted, self-managed, self-serving. But that tune has changed. Here's what to think about when contemplating a local AI strategy.

The blog post: https://thebootstrappedfounder.com/when-to-choose-local-llms-vs-apis-a-founders-real-world-guide/ The podcast episode: https://tbf.fm/episodes/390-when-to-choose-local-llms-vs-apis

Check out Podscan, the Podcast database that transcribes every podcast episode out there minutes after it gets released: https://podscan.fmSend me a voicemail on Podline: https://podline.fm/arvid

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AI-Generated Summary

Local vs. API-Based Language Models

Arvid discusses the ongoing debate around whether to use local language models or external APIs like OpenAI and Anthropic for AI integrations in business. He shares his journey of initially starting with local models due to cost efficiency but later realizing that external APIs offer better scalability and efficiency for his workload, particularly with the volume of data processed at his company, PodScan.

Defining the Right Use Cases

The podcast outlines specific scenarios where local models may still shine, particularly for quick, simple tasks requiring minimal context or complexity. Arvid shares personal anecdotes from PodScan where local models worked well initially for tasks like audio transcription, emphasizing the importance of selecting the right tool based on task complexity and context size.

Challenges with Scaling Locally

Arvid explains the difficulties encountered as the operational scale increased at PodScan, particularly when handling larger transcripts. He notes that local models can quickly become inefficient for extensive data processing and that the cost and complexity of running a local infrastructure can outweigh the benefits, particularly in high-volume environments.

Framework for Decision Making

A framework for choosing between local models and remote APIs includes several critical factors: the scale of operations, speed requirements, privacy constraints, and available resources. Arvid emphasizes that for many startups and small businesses, starting with APIs often aids in quicker validation and development before considering local implementations.