LLM Orchestration

LLM Orchestration refers to the coordinated management and control of multiple large language models (LLMs) within an AI system. It involves selecting different models for specific tasks, sequencing or parallelizing their execution, and intelligently combining their outputs. Orchestration also includes managing model switches based on cost, latency, or specialization, handling fallbacks during model failures, and maintaining context across different model calls. Modern LLM orchestration platforms enable developers to build complex AI workflows that leverage different models for reasoning, code generation, translation, or specialized domain expertise while ensuring consistent quality and performance.