How do you quantify privacy and outage derisking in the ROI of local LLM inference vs. providers API?

I'm trying to quantify the ROI of running LLM inference locally versus using the DeepSeek API.

Assume a company with 100 employees. If each employee uses about 10M input tokens and 3M output tokens per month, that is roughly:

• 1B input tokens/month
• 300M output tokens/month

Using DeepSeek’s current API pricing, that would cost approximately:

• deepseek-v4-flash: about $224/month
  • 1B input tokens × $0.14/M = $140
  • 300M output tokens × $0.28/M = $84
• deepseek-v4-pro: about $696/month
  • 1B input tokens × $0.435/M = $435
  • 300M output tokens × $0.87/M = $261

With caching, it gets even cheaper:

• 50% cache hit: $480/month
• 80% cache hit: $351/month
• 90% cache hit: $308/month
• 95% cache hit: $286/month

For local DeepSeek V4-Pro, the hardware I’m considering is something like:

• 8× NVIDIA H200 141GB single-node server
  • 1.128TB total VRAM
  • roughly $350k–$500k to buy
  • roughly $20k–$40k/month to rent 24/7 depending on provider

or possibly:

• 16× NVIDIA H100 80GB
  • 1.28TB total VRAM
  • likely $500k all-in

So purely on token cost, local inference seems very hard to justify.

The only way I can see it being justified is if we assign economic value to things like data privacy, resilience against API outages, protection from sudden quota changes, model withdrawal risk and government/export-control restrictions (like it just happened with Fable 5).

Has anyone seen a good framework for quantifying these factors economically?