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LOGICMONITOR BLOG

When AI Tools Fail: How to Map Your AI Dependencies for Proactive Visibility

AI outages are rising across ChatGPT, Gemini, and Claude. See how mapping your AI dependencies pinpoints failures fast and keeps downtime from spiking.

6–9 minutes

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AI is now production infrastructure, so treat its outages like any other critical dependency failure.

  • AI services from OpenAI, Google, and Anthropic have gone down repeatedly, sometimes simultaneously, disrupting revenue-generating workflows.

  • The cost of downtime depends on how central AI is to the path it supports, from missed trades in finance to abandoned carts in eCommerce.

  • Without a dependency map, teams waste time guessing whether a failure sits in their app, their cloud, the Internet path, or the provider.

  • Map every service your AI tools rely on now, so the next outage becomes a defined decision path rather than a scramble.

AI platforms have gone down repeatedly over the past year, and the pattern is hard to ignore. When ChatGPT, Gemini, or Perplexity stops responding, the memes come fast. Then the reality lands: if your work or your business depends on these tools, an outage can stall an entire day.

The disruptions aren’t isolated glitches. AI services have failed across different providers, often within weeks of each other:

  • February 5-6, 2025: Google’s Gemini had a 23-hour disruption that broke the “Add File” and “Link File” functions in Gems, with no workaround.
  • January 23, 2025: ChatGPT and several OpenAI APIs returned elevated error rates and “bad gateway” errors.
  • January 23, 2025: Perplexity’s API had a major outage that caused timeouts for the applications built on it.
  • December 26, 2024: A range of OpenAI services (ChatGPT, Sora, and the agents, realtime speech, batch, and DALL-E APIs) saw error rates above 90%.
  • June 4, 2024: Multiple AI platforms, including OpenAI’s ChatGPT, Anthropic’s Claude, and Perplexity, went down at the same time.

Dependence on AI has moved from a possibility to a fact of daily operations. AI now runs inside production workflows, not just experiments on the side. That shift means reliability teams need dependency visibility for the AI stack the same way they already have it for cloud, CDN, DNS, and APIs. When these systems fail, teams are left scrambling. The practical question is how to stay ahead of the next failure.

The revenue impact of AI outages

The stakes keep rising. Worldwide spending on AI is forecast to total nearly $1.5 trillion in 2025, according to Gartner. For many companies, AI applications like ChatGPT are now mission-critical to daily operations. McKinsey reports that 71% of organizations regularly use generative AI in at least one business function, from automated customer service to marketing personalization and real-time data analysis.

How much an outage costs depends on how central the AI service is to the work it supports. Where AI sits in a revenue-generating path, downtime can carry real financial weight. In finance, a few hours of disruption to an AI service used for trading or fraud detection could mean missed trades or gaps in coverage. In eCommerce, if chatbots and recommendation engines go dark during peak traffic, the potential result is abandoned carts and fewer conversions.

The impact isn’t limited to lost sales. Companies increasingly rely on AI-powered automation to streamline workflows, so an outage can force employees back to manual processes and slow productivity. That shows up clearly in customer support, where AI chatbots handle large volumes of inquiries. When an outage pushes that work back to human agents, queues grow, response times climb, and customer satisfaction can drop.

If you’re concerned about how AI outages could affect your business, now is a good time to evaluate your AI dependencies and invest in tools that help you stay ahead of disruptions.

Why visibility matters: mapping your AI dependencies

AI outages create a specific challenge. You can tell that something is broken without knowing where or why. When a request fails, teams need to answer a basic question quickly: is the problem in their own app, their cloud, the Internet path between users and the provider, or the AI provider itself? Answering that requires visibility into your AI dependencies, whether the problem starts in the application layer or somewhere in the underlying internet stack. As more products depend on external models, monitoring the AI stack becomes part of everyday reliability work.

This is where outside-in Internet visibility and inside-out infrastructure observability come together. LogicMonitor Synthetics and Internet Performance Monitoring show what users experience across the Internet path, including the third-party APIs your AI features depend on. Paired with LogicMonitor’s hybrid infrastructure observability, teams can trace a problem across the full path from user to code and see whether the failure sits inside their environment or with an external provider.

eCommerce AI dependencies: a case study

Consider an eCommerce company that uses an AI-powered chatbot for customer support. The experience depends on several key components working together:

  • Front-end CDN: delivers content quickly to users.
  • Distributed hyperscaler: acts as the origin server for dynamic content.
  • DNS and authentication services: resolve traffic and verify users before requests reach the app.
  • Search and seller APIs: retrieve relevant product data for users.
  • Chatbot powered by the OpenAI API: handles customer inquiries and provides real-time support.
  • Observability and alerting: tracks the health of each dependency and flags failures early.

The chatbot is central to the support workflow. When a shopper interacts with it, the request goes to an external API, which then calls the OpenAI API to generate a response. The chatbot’s functionality depends entirely on that OpenAI API.

Flow diagram depicting the interaction between a user, an external API, and OpenAI’s API in an eCommerce chatbot system

If the OpenAI API has an outage, the chatbot fails and customers lose support, which can lead to lost sales and damaged relationships. A clear dependency map turns that moment into a defined decision path. The team detects the failure, confirms it is the provider rather than their own stack, routes support traffic to a fallback such as a scripted flow or human agents, and notifies stakeholders. With the source confirmed as external, they can avoid pulling engineers into an internal incident escalation that wouldn’t fix anything.

How to map AI dependencies and stay ahead of outages

In the example above, the chatbot’s dependency on the OpenAI API shows why mapping AI dependencies matters. When an outage hits, knowing exactly where the failure sits can be the difference between minutes of downtime and hours of lost revenue. Mapping your dependencies helps you identify the root cause faster, which reduces downtime and limits revenue loss. Here’s how to approach it.

1. Visualize your AI dependencies

Start by mapping all the services and APIs your AI tools rely on. A useful AI dependency map includes:

  • Model provider APIs (such as OpenAI, Anthropic, or Google)
  • Vector databases that store embeddings and context
  • DNS and CDN services
  • Authentication services
  • Cloud regions hosting your workloads
  • Message queues that move requests between services
  • Payment and search APIs
  • Internal services that call or depend on the AI feature

If your chatbot depends on the OpenAI API, that dependency belongs on the map. Internet Stack Map helps you visualize these connections, so you can pinpoint where a failure occurs when an outage happens.

Internet Stack Map view

In this view of the eCommerce case study, every other service is working as expected except the OpenAI API, highlighted in red, which is affecting chatbot interactions.

2. Customize the map to your architecture

Every AI system is different, so your dependency map should reflect your specific setup. Identify key components like CDNs, DNS providers, and origin servers, and make sure they’re included. This keeps you prepared to troubleshoot issues that are specific to your environment.

3. Correlate data for faster insights

Watching an AI provider’s public status page tells you what the provider is willing to report, and it often lags the disruption your users are already feeling. To confirm impact, validate the path from your own users to the provider independently. Synthetic testing and real-time Internet outage data do exactly that. Combine those two signals with your infrastructure telemetry, and you can isolate where a failure sits much faster: in your app, your cloud, the Internet path, or the provider. That shortens diagnosis time and helps you avoid unnecessary war rooms.

This is also where AI-assisted operations help. When an AI dependency fails, Edwin AI can summarize the impact, correlate signals across telemetry, and recommend the next actions, so responders spend less time assembling context and more time resolving the issue.

4. Plan for graceful degradation

Mapping tells you where a failure sits. A fallback plan tells you what to do about it. Decide in advance how the product behaves when an AI dependency goes down: a degraded mode with reduced features, an alternate provider, cached responses for common requests, a handoff to human agents, and clear customer messaging about what to expect. Planning these paths ahead of time keeps an outage from becoming a scramble.

Faster resolution with fewer disruptions

AI outages are a reminder of how connected and interdependent modern systems have become. When they fail, every minute counts, especially if you’re losing revenue or driving customers away. The goal is straightforward: identify what broke and where faster, give the problem a clear owner, and keep teams out of war rooms they don’t need to be in.

That outcome is easier to reach inside one connected system. LogicMonitor brings together hybrid infrastructure observability, LM Internet Performance Monitoring, and Edwin AI so teams can connect user experience, Internet dependencies, and infrastructure health in one place, then move from insight to governed action. When an AI dependency degrades, that unified context helps you see who is affected and what to do next.

Map your AI dependencies before the next provider outage reaches your revenue.

LogicMonitor pairs Internet visibility with infrastructure observability, so you can trace any AI failure from user to provider in one place. See exactly where a problem sits and route around it fast.

FAQs

What causes AI service outages like the ones affecting ChatGPT and Gemini?

AI outages can originate at multiple layers: the model provider’s API, DNS or CDN services, authentication, cloud regions, or the Internet path between users and the provider. Because the failure point isn’t always obvious, teams need dependency visibility to tell whether the issue is internal or with an external provider.

How do I map my AI dependencies?

Start by listing every service your AI tools rely on, including model provider APIs, vector databases, DNS and CDN, authentication, cloud regions, message queues, and payment or search APIs. Tools like Internet Stack Map visualize these connections, so you can pinpoint where a failure occurs during an outage.

What should happen when an AI dependency goes down?

Plan for graceful degradation in advance. Options include a reduced-feature mode, an alternate provider, cached responses for common requests, a handoff to human agents, and clear customer messaging. Deciding these paths ahead of time keeps an outage from turning into a scramble.

By Denton Chikura

Technical Writer

Denton Chikura is a technical writer and longtime observability advocate focused on helping site reliability engineers and engineering teams discover the tools and capabilities that strengthen internet resilience. He works at the intersection of monitoring, performance, and infrastructure to make complex systems more understandable and usable, bridging the gap between deep technical detail and real‑world operations. His goal is to help teams build faster, detect issues earlier, and recover smarter, ultimately making the internet a better, more reliable place for everyone.

Disclaimer: The views expressed on this blog are those of the author and do not necessarily reflect the views of LogicMonitor or its affiliates.

© LogicMonitor 2026 | All rights reserved. | All trademarks, trade names, service marks, and logos referenced herein belong to their respective companies.

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