Self-Hosted LLMs for the Enterprise: Is Open Source the Path to Compliance?

As enterprises embrace the power of generative AI, concerns around data privacy, regulatory compliance, and vendor lock-in are more pressing than ever. What if you could deploy Large Language Models entirely within your network perimeter—retaining complete control, eliminating external data exposure, and enabling audit-ready operations? My latest POC explores this possibility by deploying open source LLMs like Llama 4 Scout and Code Llama on GCP infrastructure, leveraging a security-first architecture with Vertex AI, GKE, and Private Service Connect. Are organizations seriously evaluating self-hosted AI as a scalable, compliant alternative to proprietary models? I’m curious to hear how others are approaching internal deployments to meet data sovereignty and industry-specific regulatory requirements, especially in sectors such as healthcare and finance. Is open source poised to power the next wave of enterprise AI?