Self-hosted LLM
A self-hosted LLM is an open-weight model you run on your own infrastructure, so data never leaves your environment.
Self-hosting runs an open-weight model on your own cloud or on-prem hardware, giving you data residency and control at the cost of running and scaling inference yourself.
It’s the answer when regulation or data sensitivity closes the hosted-API path, or when very high volume makes fixed infrastructure cheaper than per-token pricing. Open-weight frontier models are strong, though the absolute quality ceiling is still set by the top hosted models.
A complete private stack usually includes inference serving, RAG pipelines over your data, optional fine-tuning, and the observability and cost controls you’d expect from any production system.