Multi-Cloud Semantic Orchestration of AI-Driven Pipelines for Privacy-First LLM Inference

Because more people need scalable and private Large Language Model (LLM) processing in the cloud, better orchestration methods for using different cloud resources are required. This research proposes a new architecture for handling Multi-Cloud Semantic Orchestration of AI-driven data pipelines to make LLM inference workflows more efficient, secure and in line with company policies. The framework makes it possible to effectively assign tasks and control data with the help of ontology-based knowledge graphs and added metadata by semantic enrichment. Our system uses distributed coordination engines, ensures both differential privacy and secure multiparty computation and selects appropriate models for proper latency, throughput and compliance. Experimental findings from using well-known NLP datasets show that the orchestration strategy proposed here reduces latency by up to 38 % and ensures more privacy by 22 % than standard single-cloud systems. This research supports better LLM operations (LLMOps) in areas like healthcare and finance, since both need to pay close attention to data protection and privacy laws.