LLMOps at Scale: Reliability, Cost Governance, and Multi-Model Orchestration for Enterprise Generative AI Platforms

Abstract

Enterprise deployments of large language models (LLMs) expose a class of operational challenges that differ fundamentally from classical machine-learning services: probabilistic outputs, prompt sensitivity, token-based costs that scale with request volume, multi-provider availability variability, and the absence of a single universal quality metric. This paper presents a formal LLMOps framework for reliable, cost-governed, multi-model orchestration in enterprise environments. We introduce a policy-aware control plane, a multi-objective model router that selects among small, domain-tuned, and large model tiers, an evaluation-driven release pipeline, and a token-budget governance loop. Model selection is formalized as a constrained optimization over latency, quality, cost, safety risk, and service-level objectives (SLOs). We evaluate the framework on five enterprise workload families—question answering, summarization, code assistance, retrieval-augmented generation (RAG), and workflow automation—on a Kubernetes-based testbed. Experiments with 30 repeated runs per configuration show that the proposed policy-aware router reduces p95 latency by 43.8% and average inference cost by 48.9% relative to a single-large-model baseline, while raising the automated quality score from 0.78 to 0.86 and the SLO pass rate from 86% to 96%. Ablation studies confirm that each major subsystem—cost policy, quality evaluation, semantic cache, and fallback orchestration—contributes measurably and independently to overall reliability. Statistical tests (Mann-Whitney U, p < 0.001) confirm all primary comparisons.