Token prices won’t increase if you host your own LLMs

Rising and uncertain cloud LLM token costs, along with growing dependence on token-intensive agent workflows, are presented as reasons organizations may consider self-hosting models rather than relying solely on providers such as OpenAI or Anthropic. Self-hosting can provide greater control over costs, availability, model versions, privacy, customization, and interpretability, while allowing workflow tools such as n8n to swap model providers without redesigning surrounding logic. However, it also shifts responsibility for security, infrastructure setup, runtime compatibility, updates, reliability, performance, and resource management to the organization. Models can be deployed locally, on organizational hardware, or on rented GPU and CPU cloud infrastructure, using runtimes including Ollama, llama.cpp, vLLM, SGLang, and LM Studio depending on deployment and workload needs. For many automation use cases, quantized 3B–13B parameter open models can balance capability and hardware requirements, with Llama, Qwen, Mistral, Gemma, and SmolLM highlighted for general tasks, coding, multilingual work, routing, and structured tool calling.

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