Self-Hosted LLM Guide: Setup, Tools & Cost Comparison (2026)

Enterprise spending on LLMs has exploded. Model API costs alone doubled to $8.4 billion in 2025, and 72% of companies plan to increase their AI budgets further this year. But there's a problem. According to Kong's 2025 Enterprise AI report, 44% of organizations cite data privacy and security as the top barrier to LLM adoption. Every prompt sent to OpenAI, Anthropic, or Google touches external servers. For companies handling sensitive data, that's a dealbreaker. Self-hosting solves this. When you run an LLM on your own infrastructure, your data never leaves your environment. No third-party retention policies. No training on your inputs. No compliance gray areas. The tradeoff is complexity. Self-hosting LLMs requires choosing the right model, sizing your hardware, configuring deployment tools, and maintaining the stack over time. It's not plug-and-play like calling an API. This guide walks you through the process. You'll learn what self-hosting actually involves, what hardware you need,