Enterprise AI Analysis of FAILS: A Framework for Automated Collection and Analysis of LLM Service Incidents
Authored by Sándor Battaglini-Fischer, Nishanthi Srinivasan, Bálint László Szarvas, Xiaoyu Chu, and Alexandru Iosup
Executive Summary: Why LLM Reliability is a Boardroom Issue
The research paper, "FAILS: A Framework for Automated Collection and Analysis of LLM Service Incidents," presents a pioneering open-source framework for systematically monitoring the reliability of major Large Language Model (LLM) services like ChatGPT and Claude. As enterprises increasingly integrate these powerful AI tools into critical workflows, their operational dependency grows, making service downtime not just an inconvenience but a significant business risk. The FAILS framework addresses a critical gap by providing a structured methodology to collect and analyze incident data directly from providers, moving beyond anecdotal reports to data-driven reliability assessment.
From an enterprise perspective at OwnYourAI.com, this research is invaluable. It validates the urgent need for robust AI observability and introduces key performance indicators (KPIs) like Mean Time To Recovery (MTTR) and Mean Time Between Failures (MTBF) as essential metrics for vendor selection and risk management. The papers findingsthat different providers exhibit vastly different failure patterns and recovery speedsunderscore the necessity of a custom, proactive monitoring strategy. This analysis translates the academic framework of FAILS into actionable enterprise intelligence, demonstrating how a similar approach can de-risk AI adoption, optimize vendor contracts, and ensure the resilience of custom AI solutions that drive business value.
The Enterprise Challenge: The High Cost of AI Downtime
As organizations embed LLMs into customer service bots, internal knowledge bases, and complex data analysis pipelines, the stability of these third-party services becomes paramount. An outage isn't just a technical glitch; it's a direct hit to productivity, customer satisfaction, and revenue. Relying on public status pages or user-driven complaint sites is a reactive, insufficient strategy. What enterprises require is a proactive, analytical approach to understand:
- Which AI provider is genuinely the most reliable for our specific needs?
- How quickly do different vendors resolve critical incidents?
- What is the risk of a cascading failure affecting multiple AI services we depend on?
- How can we build resilient systems that anticipate and mitigate these external failures?
The FAILS paper provides the foundational blueprint for answering these questions with data, not guesswork.
Deconstructing the FAILS Framework: An Enterprise Blueprint for AI Observability
The FAILS framework is more than an academic tool; it's a model for a comprehensive enterprise AI reliability monitoring system. Its architecture, which we've re-conceptualized for a business context below, consists of a data collection pipeline, an analysis engine, and an intuitive interface for decision-makers.
- Data Collection & Aggregation: The system automatically ingests incident data from multiple sourcesnot just public LLM providers but also internal, custom-built AI services. This creates a single source of truth for all AI-related operational events.
- Analysis Engine: This is the core of the framework. It processes the raw data to calculate critical reliability metrics. This engine moves beyond simple uptime percentages to provide deep insights into failure characteristics.
- Business Intelligence Layer: The results are presented in an executive-friendly dashboard. This layer visualizes trends, compares vendor performance, and can be configured to send automated alerts to stakeholders when a critical service degrades.
Key Reliability Metrics Revealed by the Research
The FAILS paper champions three core metrics that are essential for any enterprise managing AI services. Here's what they are and why they matter for your business.
Is Your AI Vendor as Reliable as You Think?
Our custom AI observability solutions can help you track these metrics for the services you depend on. Don't wait for an outage to find out your risks.
Book a Reliability AuditInteractive Data Insights: A Comparative Look at LLM Providers
Using the methodology from the FAILS paper, we can analyze publicly available data to compare the reliability of major LLM providers. The following interactive charts are based on the data collected and analyzed in the study, rebuilt here to highlight key enterprise takeaways.
Incident Volume: A Proxy for Complexity and Usage
This chart shows the total number of reported incidents over the study period. While a higher number isn't inherently badit often correlates with a larger user base and more complex systemsit does indicate a higher frequency of potential service disruptions. OpenAI leads in volume, reflecting its market position.
System Stability (MTBF): Who Stays Up Longer?
Mean Time Between Failures (MTBF) measures the average time a provider's service operates correctly between outages. A higher MTBF is better, indicating greater stability. The research shows newer or less complex services like Character.AI exhibit significantly higher stability than incumbents.
Recovery Speed (MTTR): Who Gets Back Up Faster?
Mean Time To Recovery (MTTR) is the average time it takes to resolve a failure once it occurs. A lower MTTR is critical for business continuity. The data reveals that Stability.AI recovers remarkably fast, while others can take hours, impacting dependent business processes.
Systemic Risk: The Co-occurrence of Failures
This analysis reveals how many services are typically affected during a single incident. A high rate of multi-service failures (like with Anthropic) suggests tightly coupled systems and a higher risk of widespread disruption. Providers with more compartmentalized, single-service failures (like OpenAI and Stability.AI) may offer better risk isolation.
ROI of Proactive Incident Analysis: From Reactive Costs to Proactive Savings
Implementing a FAILS-inspired monitoring framework isn't an academic exercise; it's a strategic investment. By proactively identifying reliability risks, you can mitigate costly downtime, optimize developer resources, and negotiate better terms with vendors. Use our calculator to estimate the potential savings.
Your Enterprise Roadmap to AI Reliability
Adopting a data-driven approach to AI observability is a journey. Based on the principles of the FAILS framework, here is a phased roadmap OwnYourAI.com recommends for enterprises.
Test Your AI Reliability Knowledge
Think you've got a handle on the key concepts? Take our short quiz to see how well you understand the metrics that matter for enterprise AI resilience.
Ready to Build a Resilient AI Strategy?
The insights from the FAILS research are a starting point. The real value comes from applying these principles to your unique technology stack and business goals. Let's discuss how OwnYourAI.com can help you build a custom AI observability and reliability framework.
Schedule a Custom Strategy SessionReady to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat