An Empirical Study of Production Incidents in Generative AI Cloud Services

2025-04-11

AI Summary

This study addresses reliability challenges in generative AI (GenAI) cloud services by conducting the first large-scale empirical analysis of production incidents, leveraging four years of incident data from a leading provider to characterize the full incident lifecycle. Using a mixed-methods approach—combining qualitative coding with quantitative statistical analysis—the work identifies critical root causes, including hardware dependency, tight model-service coupling, and prompt engineering sensitivity; defines novel failure dimensions such as content quality degradation and privacy leakage; and proposes a GenAI-specific incident taxonomy and impact assessment matrix. The findings expose persistent open challenges in incident detection, triage, and mitigation for GenAI systems. This work establishes the first empirically grounded benchmark and theoretical foundation to guide both industrial operations and academic research on GenAI reliability.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Reasoning under Uncertainty: Causality
• Natural Language Processing: Generation

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity

Abstract

The ever-increasing demand for generative artificial intelligence (GenAI) has motivated cloud-based GenAI services such as Azure OpenAI Service and Amazon Bedrock. Like any large-scale cloud service, failures are inevitable in cloud-based GenAI services, resulting in user dissatisfaction and significant monetary losses. However, GenAI cloud services, featured by their massive parameter scales, hardware demands, and usage patterns, present unique challenges, including generated content quality issues and privacy concerns, compared to traditional cloud services. To understand the production reliability of GenAI cloud services, we analyzed production incidents from a leading GenAI cloud service provider spanning in the past four years. Our study (1) presents the general characteristics of GenAI cloud service incidents at different stages of the incident life cycle; (2) identifies the symptoms and impacts of these incidents on GenAI cloud service quality and availability; (3) uncovers why these incidents occurred and how they were resolved; (4) discusses open research challenges in terms of incident detection, triage, and mitigation, and sheds light on potential solutions.

Problem

• Analyzing production incidents in GenAI cloud services
• Identifying symptoms and impacts on service quality
• Exploring incident causes and resolution strategies

Innovation

• Analyzed GenAI incidents over four years
• Identified symptoms and impacts on quality
• Discussed incident detection and mitigation solutions