Choosing the Right LLM for Business – Security and Hosting Considerations

Not all large language models (LLMs) are created equal—and businesses that overlook this are playing Russian roulette with their data. Open-source, locally deployable models like Meta’s LLaMA 2, Databricks' Dolly 2.0, and BigScience’s BLOOM offer maximum control and privacy. They allow sensitive information to remain securely behind firewalls, accessible and modifiable by internal teams.

On the flip side, cloud-only models like OpenAI’s GPT-4 and Google's Bard, while powerful, come with privacy risks. By definition, these solutions store and process data externally, introducing vulnerabilities. High-profile issues around unclear data-handling policies have already forced corporations like Apple and Verizon to ban cloud-based public LLMs internally.

The choice of LLM isn’t simply technical—it’s strategic. If you value security, compliance, and control, your AI needs to live locally. Local LLMs aren’t just a tech preference; they're a foundational business decision safeguarding your data and trust.