Stop Choosing Between Local and Cloud LLMs: A Field Guide to Hybrid Patterns

Hybrid workflows combining local and cloud-based Large Language Models (LLMs) offer a way to balance powerful reasoning capabilities with data privacy. This approach involves using a local model to handle sensitive information while a cloud model performs more complex tasks. Five distinct patterns are detailed, including "Sanitize-and-Solve" for privacy and "Escalate-on-Hard" for cost efficiency. A hands-on case study demonstrates how to use a local Gemma 4 model to anonymize a smart-home scheduling problem before sending it to a cloud LLM for the optimal solution.