Self-Hosted LLM vs. Cloud APIs: How to Route Between Them

Self-hosting open-weight LLMs gives organizations greater control over data processing and residency, which can be valuable for sensitive workloads, but it also introduces hardware, maintenance, and operational responsibilities that cloud APIs largely manage. Model selection should begin with testing models for a specific task and choosing the smallest reliable option, since larger parameter counts generally require more VRAM and compute; quantization can lower memory requirements but may reduce performance on demanding tasks. Suggested models range from lightweight Phi-4-mini and Qwen3 8B deployments to higher-capacity options such as Mistral Small 3.1 and Llama 4 Scout, with hardware needs varying from roughly 8 GB to 80 GB of VRAM. A typical self-hosted stack combines GPU hardware, a model, an inference runtime such as Ollama or vLLM, an optional retrieval-augmented generation layer, and an application interface. Organizations should weigh privacy and compliance needs, total operating costs, usage volume, latency, and access to frontier-model capabilities when deciding between local and cloud inference. Hybrid systems can route sensitive, routine, or predictable workloads to local models while sending more complex tasks to cloud services, and n8n can implement and test this routing through workflow rules, local Ollama connections, cloud model nodes, and sensitivity checks.

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