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The Amazon S3 Outage - When the Internet's Hard Drive Fails …

Denis Goodwin

Last week, SmartBear observed a sudden and protracted 5X increase in web page timeout errors associated with the failure of Amazon's S3 cloud-based storage service. Looking a bit more closely at our data, we dug up a few more interesting angles on the impact of the failure.

Event Timeline

The issue hit suddenly – we saw an immediate spike in errors at 12:35 p.m. EST, and by 12:45 p.m. EST, error rates were 5X normal. For some specific types of timeout errors, the spike was more than 10X normal. At 3:30 p.m. EST, the error rate began dropping and by 3:50 p.m. EST, rates had returned to normal.


Web vs. API

The issue hit web pages hard, while API monitors were not noticeably impacted by this outage. Web pages and web apps often utilize content storage hosted by cloud services such as Amazon S3.

Common failure scenarios on Tuesday included page elements failing to load, which could cause either the whole web page to time out or specific content on a page might not render. Depending on the design of a given page, this partial content failure could be relatively minor or it could render a critical web journey non-functional. File uploads and downloads that rely on S3 storage endpoints were particularly hard hit.

In order to get a complete picture of application health, it's necessary to monitor your real user's journey through the application. The monitored user journeys that depended heavily on content hosted in S3 failed. Those that didn't have that dependency continued functioning. I personally experienced this with Slack – I was able to use the app, however files could not be uploaded presumably because these files are stored by Slack using S3 as the storage mechanism.

While far less pronounced than the spike in errors, some response time degradation was observed in API monitors that continued running successfully. Given that the issue affected Amazon's storage services rather than their hosting services for applications, this makes sense.

Geographic Impact

The issue was more acutely felt in the United States, but we observed impacts all over the globe. The spike in page errors was seen on websites dependent on Amazon S3, many of which are U.S.-hosted websites that are likely monitored from U.S. locations. Unsurprisingly, error counts spiked by as much as 25X in some U.S. monitoring locations. While not as significant as the U.S. locations, timeout and page error increases were also observed from Canada, Europe and Asia.

Takeaways

Much of the web is built on the backs of cloud providers. Most of the time, these cloud services provide a great user experience. Amazon will learn from the root cause of this issue and likely emerge from this outage more resilient than ever. It's impossible to control all aspects of these shared services – but here are three steps to take that are in your control.

1. Identify your business critical applications

2. Proactively monitor user journeys on these applications

3. Don't rely on your third party provider to tell you when it is down

It is key to utilize independent monitoring services to ensure your applications are up, functioning correctly and fast. Furthermore, missing content can be catastrophic or merely inconvenient to a critical user journey – it's important that your monitoring tool can be configured to know the difference.

Denis Goodwin is Director of Product Management, AlertSite, SmartBear Software.

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The Amazon S3 Outage - When the Internet's Hard Drive Fails …

Denis Goodwin

Last week, SmartBear observed a sudden and protracted 5X increase in web page timeout errors associated with the failure of Amazon's S3 cloud-based storage service. Looking a bit more closely at our data, we dug up a few more interesting angles on the impact of the failure.

Event Timeline

The issue hit suddenly – we saw an immediate spike in errors at 12:35 p.m. EST, and by 12:45 p.m. EST, error rates were 5X normal. For some specific types of timeout errors, the spike was more than 10X normal. At 3:30 p.m. EST, the error rate began dropping and by 3:50 p.m. EST, rates had returned to normal.


Web vs. API

The issue hit web pages hard, while API monitors were not noticeably impacted by this outage. Web pages and web apps often utilize content storage hosted by cloud services such as Amazon S3.

Common failure scenarios on Tuesday included page elements failing to load, which could cause either the whole web page to time out or specific content on a page might not render. Depending on the design of a given page, this partial content failure could be relatively minor or it could render a critical web journey non-functional. File uploads and downloads that rely on S3 storage endpoints were particularly hard hit.

In order to get a complete picture of application health, it's necessary to monitor your real user's journey through the application. The monitored user journeys that depended heavily on content hosted in S3 failed. Those that didn't have that dependency continued functioning. I personally experienced this with Slack – I was able to use the app, however files could not be uploaded presumably because these files are stored by Slack using S3 as the storage mechanism.

While far less pronounced than the spike in errors, some response time degradation was observed in API monitors that continued running successfully. Given that the issue affected Amazon's storage services rather than their hosting services for applications, this makes sense.

Geographic Impact

The issue was more acutely felt in the United States, but we observed impacts all over the globe. The spike in page errors was seen on websites dependent on Amazon S3, many of which are U.S.-hosted websites that are likely monitored from U.S. locations. Unsurprisingly, error counts spiked by as much as 25X in some U.S. monitoring locations. While not as significant as the U.S. locations, timeout and page error increases were also observed from Canada, Europe and Asia.

Takeaways

Much of the web is built on the backs of cloud providers. Most of the time, these cloud services provide a great user experience. Amazon will learn from the root cause of this issue and likely emerge from this outage more resilient than ever. It's impossible to control all aspects of these shared services – but here are three steps to take that are in your control.

1. Identify your business critical applications

2. Proactively monitor user journeys on these applications

3. Don't rely on your third party provider to tell you when it is down

It is key to utilize independent monitoring services to ensure your applications are up, functioning correctly and fast. Furthermore, missing content can be catastrophic or merely inconvenient to a critical user journey – it's important that your monitoring tool can be configured to know the difference.

Denis Goodwin is Director of Product Management, AlertSite, SmartBear Software.

Hot Topics

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...