Adaptive Reliability and Error Budget Governance for Large-Scale Language Model Inference Systems
Keywords:
Site reliability engineering, error budget management, large language models, service-level objectivesAbstract
The rapid industrialization of large-scale language model inference has transformed the operational landscape of digital services, pushing traditional reliability engineering paradigms into unprecedented complexity. Contemporary artificial intelligence platforms are no longer monolithic computational services; they are distributed, heterogeneous, and deeply interwoven with user experience, data gravity, and real-time quality-of-service constraints. This article develops a comprehensive theoretical and empirical framework for understanding how site reliability engineering practices, particularly error budget management, must evolve to remain effective in the era of large-scale language model serving. Building on recent advances in large language model systems engineering, cloud-native service-level objective orchestration, and error-budget-driven reliability governance, the paper articulates a unified perspective that integrates infrastructural elasticity, memory management, and inference routing into a single reliability economics model.
The study is grounded in a detailed synthesis of recent research on language model inference pipelines, including GPU and CPU offloading strategies, scheduling architectures, long-context processing, and streaming quality of experience. These technical developments are examined through the lens of site reliability engineering, where error budgets serve not merely as operational constraints but as strategic instruments for balancing innovation velocity with user trust. A central conceptual contribution of this work is the reinterpretation of error budgets as multidimensional governance constructs that encompass latency, availability, accuracy, fairness, and contextual coherence rather than only uptime. This conceptualization is directly aligned with contemporary reliability thinking in large-scale systems, particularly the error budget management paradigm articulated in modern site reliability engineering literature (Dasari, 2025).
Methodologically, the paper employs a qualitative systems synthesis approach, integrating architectural analyses, operational theory, and service-level objective modeling to derive an interpretive framework for adaptive reliability. Rather than relying on numerical simulation, the study develops descriptive and inferential arguments that map how inference pipelines, scheduling strategies, and routing mechanisms collectively determine the consumption and replenishment of error budgets. The results demonstrate that advanced scheduling and memory management techniques can be interpreted as implicit reliability controls that redistribute error budget expenditure across time, users, and workloads.
The discussion extends these findings into a broader theoretical debate about the future of reliability engineering in AI-driven infrastructures. It argues that traditional binary notions of failure are inadequate for generative systems whose outputs are probabilistic, contextual, and socially embedded. By positioning error budgets as socio-technical contracts between service providers and users, the article offers a foundation for reliability governance that is both technically rigorous and ethically responsive. The paper concludes by outlining implications for cloud-native architecture, regulatory compliance, and the design of next-generation service-level objectives in AI platforms.
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