On-Prem vs. Proxy — How to Deploy LLMs Without Leaking Sensitive Data
Your SOC 2 cert covers the vendor’s infrastructure — not what your users paste into prompts. The moment someone feeds client data into a cloud model, the liability is yours. The fix is architectura...
Source: DEV Community
Your SOC 2 cert covers the vendor’s infrastructure — not what your users paste into prompts. The moment someone feeds client data into a cloud model, the liability is yours. The fix is architectural. Here are the three options, and when to use each. On-Premise Model runs on your hardware. Nothing leaves your network. The only option that satisfies air-gap requirements and strict data residency mandates. Use it when: •Air-gap or strict residency mandate applies •Gov / defense / intelligence data involved •>2M tokens/day makes infra TCO competitive with API spend Reality check: $80K–$250K+ upfront · 3–6 months to production · 0.5–1 FTE DevOps ongoing · hardware refresh every 3–4 years OpenAI-compatible endpoint on your own hardware python -m vllm.entrypoints.openai.api_server \ --model meta-llama/Llama-4-Scout-17B-16E-Instruct \ --host 0.0.0.0 --port 8000 --tensor-parallel-size 4 Proxy / Gateway Doesn’t move the model — moves the control plane. Every request flows through a central ga
