Planning an AI-powered project? Your deployment choices just got more interesting.

It's easy to assume LLM models are either fully cloud-hosted or on-premises.

But there's a spectrum of options that could give you the best of both worlds.

I've put together a basic LLM deployment matrix that breaks down key factors across five deployment models:

1. Shared, Remotely Hosted

2. Dedicated, Remotely Hosted

3. Hybrid (Local Inference, Cloud Model)

4. Locally Hosted

5. On-Premise Managed Services

The matrix covers dimensions like privacy, cost, performance, control, and scalability. It's a starting point to help you navigate the trade-offs and find the sweet spot for your specific needs.

For instance, did you know that hybrid models can offer high privacy and performance with variable costs? Or that dedicated remote hosting can provide a balance of control and scalability?

This isn't just about security - it's about optimizing your AI operations for your unique context.

What factors are most critical for your AI projects? How might this matrix weigh on your deployment decisions?