Cross-Cloud LLMOps Scheduler for Privacy-Budgeted RAG and Inference

Enterprise retrieval-augmented generation (RAG) and large-language-model inference increasingly run across multiple cloud providers, vector stores, data catalogs, and model endpoints. The operational scheduler must therefore optimize latency and cost without violating data residency, gateway intent policies, model-context-protocol contracts, or privacy budgets. This paper proposes a Cross-Cloud LLMOps Scheduler (CCLS), a synthetic architecture for routing extraction, retrieval, context construction, and inference under explicit privacy accounting and evidence-maintenance constraints. CCLS extends Policy-Verified Agentic DataOps for Regulated Multi-Cloud Analytics by applying policy-verified execution to LLM workloads, and it extends Retrieval-Grounded Documentation Agents for Enterprise Compliance Evidence by treating compliance evidence freshness as a first-class scheduling signal. The scheduler combines governed API intents, cross-cloud workload placement, distributed RAG, MCP tool contracts, anonymized evidence views, and latency-aware sequence models for context selection. We define the architecture, a multi-objective scheduling model, and a simulated benchmark over compliance Q&A, operational summarization, and governed decision-support traffic. In simulation, CCLS reduces P95 request latency by 40.5% relative to static approved-cloud routing, improves evidence-supported answer recall from 82.4% to 91.8%, and eliminates privacy-budget overruns and unauthorized policy violations.