When the internet breaks, it doesn’t usually happen because a single website tripped over its own feet. It happens because the floor disappeared. In the modern web, that floor is often Amazon Web Services. When you’re staring at a "503 Service Unavailable" screen or your smart fridge suddenly forgets how to be smart, the question why did aws go down starts trending within minutes. It's almost a ritual now. People flock to Twitter (or X, if we're being technical) to see if everyone else is also unable to load their doorbell camera or access their corporate Slack channels.
The cloud is just someone else's computer. We know this. But it's actually thousands of "someone else's" computers packed into massive, windowless warehouses in places like Northern Virginia (US-EAST-1) or Oregon. When these systems fail, it’s rarely because of a single "oops" moment. It’s a cascading failure of logic, scale, and sometimes, a very tired engineer typing the wrong command.
Honestly, the scale is the problem. AWS is so big that its own management tools sometimes struggle to keep up with itself. It’s like a city that grew so fast it forgot where it buried the power lines.
The Northern Virginia Problem: US-EAST-1
If you want to understand why did aws go down during most major outages, you have to look at Northern Virginia. This region, known as US-EAST-1, is the oldest and most densely packed part of the Amazon empire. Because it was the first, almost every major legacy company has their data there. It’s the "default" setting for many developers.
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In December 2021, a massive outage hit this region that took down everything from Disney+ to Roomba vacuums. The cause? An automated scaling activity. Basically, AWS has internal services that help its own network grow and shrink. A bug in that automation caused a surge of connection attempts that overwhelmed the internal network devices. It’s the digital equivalent of a crowd crush at a concert. The routers couldn’t talk to each other because they were too busy being yelled at by other routers.
The terrifying part of that 2021 event wasn't just that the services went down. It was that the dashboard telling people things were down was also down. Amazon's internal monitoring relied on the very network that was failing. You can't fix a house if the front door is jammed and the key is inside.
The Kinesis Chaos of 2020
Go back a bit further to November 2020. This was another classic example of "why did aws go down" that left developers pulling their hair out. The culprit was Amazon Kinesis. This is a service that handles big data streams in real-time.
Amazon was trying to add a little bit of extra capacity to the Kinesis fleet. Nothing crazy. Just a standard upgrade. However, as they added more servers, they hit an operating system limit on the number of threads (essentially tiny processing lanes) that the back-end servers could handle. Once that limit was hit, the whole system choked.
- It wasn't a hardware failure.
- It wasn't a cyberattack.
- It was just a software limit that nobody realized existed until the system reached a specific, massive scale.
This triggered a "positive feedback loop" in the worst way possible. When one part of the system slowed down, the parts relying on it started retrying their requests. This doubled the traffic. Then tripled it. Pretty soon, the whole region was underwater.
Is it Always a Human Error?
Usually, yeah. But not in the way you think. It's not someone spilling coffee on a server rack. It's usually a "fat-finger" error in a configuration file or a script that does exactly what it was told to do, even if what it was told to do was catastrophic.
Back in 2017, an authorized S3 team member was debugging an issue with the billing system. They executed a command intended to remove a small number of servers. A typo in the command removed a much larger set of servers than intended. Those servers supported two other functional subsystems.
The result? S3, the "storage" backbone of the internet, went dark.
Since so many other AWS services—and millions of websites—rely on S3 to load images, scripts, and data, the internet basically hit a brick wall. People couldn't even log into the AWS console to try and fix their own apps because the console itself needed S3 to function. This is what engineers call a "circular dependency." It's a nightmare scenario where the tool you need to fix the break is broken by the break itself.
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The Complexity Tax
Modern cloud architecture is incredibly complex. We’ve moved away from "monoliths" (one big app) to "microservices" (thousands of tiny apps talking to each other).
When you ask why did aws go down, the answer is often found in the "control plane." Think of the data plane as the highway and the control plane as the air traffic controllers. Most AWS outages happen in the control plane. The servers (the highway) might actually be fine, but the system that tells the cars where to go has lost its mind.
In 2023, we saw several smaller "hiccups" that were related to regional connectivity. Fiber optic cables get cut by construction crews. Power grids fail. But AWS usually survives those because they have redundant everything. The outages that make the news are almost always logic errors.
Why We Can't Just "Move"
You might think, "If US-EAST-1 is so flaky, why not just leave?"
It's not that simple.
Cost is one thing. Data egress fees (the price Amazon charges you to move your data out of their cloud) are notorious. But the real reason is "gravity." Your database is in Virginia. Your users are on the East Coast. Moving petabytes of data to Oregon or Ireland is a massive, expensive, and risky project.
Most companies choose to stay and just hope the "Big One" doesn't hit today. They try to build "multi-region" setups, but that's incredibly hard to manage. If your app is running in two places at once, you have to make sure the data is perfectly synced. If it’s not, you get "split-brain," where half your users see one thing and half see another. That's often worse than a total outage.
The Myth of 99.99% Availability
Amazon offers Service Level Agreements (SLAs). They promise "four nines" or "five nines" of uptime.
99.99% sounds great.
But do the math.
That still allows for about 52 minutes of downtime every year. And that's usually calculated as an average over a month. If AWS goes down for 4 hours once a year, they might technically violate their SLA, but what do you get? A credit on your next bill. That credit doesn't cover the millions of dollars lost in sales for a company like Netflix or Airbnb.
The reality is that why did aws go down is a question that will keep being asked as long as we centralize the entire internet into the hands of three or four companies. We've traded the fragility of owning our own servers for the systemic risk of sharing someone else's.
How to Prepare for the Next One
You can't stop AWS from failing. If Amazon's own engineers can't stop it, you certainly can't. But you can change how your business reacts when the "AWS is Down" notification hits your phone.
- Static Fallbacks: If your main app goes down, can you serve a "read-only" version of your site from a different provider like Cloudflare or Vercel?
- Decouple Your Services: Don't let a failure in your logging service take down your entire checkout process. Use "circuit breakers" in your code so that if one part of the cloud is slow, your app just skips that feature instead of hanging forever.
- Monitor the Right Things: Stop checking the official AWS Status Page. It’s notoriously slow to update. Use third-party tools or "canary" scripts that alert you the moment your users are seeing errors.
- Multi-Cloud Strategy: It’s the "holy grail" but it’s expensive. Having a backup on Google Cloud or Azure is the only true way to survive a total AWS regional collapse. For 90% of companies, the cost of building this isn't worth it until the day the outage actually happens.
The next time the internet feels like it’s melting, remember that it's likely just a ripple effect from a data center in Virginia. AWS is a marvel of engineering, but it’s still just software written by humans. And humans make mistakes.
Next Steps for Reliability:
- Audit your dependencies: Map out which of your critical services rely specifically on US-EAST-1 and see if you can migrate non-latency-sensitive workloads to US-WEST-2.
- Chaos Engineering: Use tools to purposefully "break" parts of your staging environment to see if your app stays up. If it doesn't, you've found your weak point before Amazon finds it for you.
- Review your SLA: Read the fine print on your AWS agreement. Understand exactly what you are—and aren't—compensated for during a major outage.