Self-Hosted LLM

Running an open-source large language model on infrastructure you control—your own GPUs, your VPC, or on-prem hardware—instead of calling a managed API. Self-hosting is typically chosen for data residency and compliance requirements, very high token volumes where dedicated inference is cheaper than per-token API pricing, or workloads that need fine-tuning beyond what hosted providers expose.