390: When to Choose Local LLMs vs APIs

Key takeaways

Start with APIs for efficiency and scalability in early stages of business.
Local models can be suitable for small, quick tasks but become burdensome at scale.
Always assess the privacy implications and compliance needs related to data handling.
The choice between local and API-based models should prioritize practicalities and the unique demands of your business.

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.

Show notes

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.fm
Send me a voicemail on Podline: https://podline.fm/arvid

You'll find my weekly article on my blog: https://thebootstrappedfounder.com

Podcast: https://thebootstrappedfounder.com/podcast

Newsletter: https://thebootstrappedfounder.com/newsletter

My book Zero to Sold: https://zerotosold.com/

My book The Embedded Entrepreneur: https://embeddedentrepreneur.com/

My course Find Your Following: https://findyourfollowing.com

Here are a few tools I use. Using my affiliate links will support my work at no additional cost to you.
- Notion (which I use to organize, write, coordinate, and archive my podcast + newsletter): https://affiliate.notion.so/465mv1536drx
- Riverside.fm (that's what I recorded this episode with): https://riverside.fm/?via=arvid
- TweetHunter (for speedy scheduling and writing Tweets): http://tweethunter.io/?via=arvid
- HypeFury (for massive Twitter analytics and scheduling): https://hypefury.com/?via=arvid60
- AudioPen (for taking voice notes and getting amazing summaries): https://audiopen.ai/?aff=PXErZ
- Descript (for word-based video editing, subtitles, and clips): https://www.descript.com/?lmref=3cf39Q
- ConvertKit (for email lists, newsletters, even finding sponsors): https://convertkit.com?lmref=bN9CZw