Ghosts may not be real but the downtimes are and they are super scary in Tech. This October was full of it.
The day the internet took a nap
You know that
“the cloud is just someone else’s computer”
Well, that “someone else” apparently forgot to plug it back in.
First it was AWS taking a quick nap mid-week. Then, like it was trying to one-up its rival, Microsoft Azure and 365 decided to join the outage Olympics. Teams? Down. Outlook? Gone. Even OneDrive took the day off. It was like watching half the internet collectively faceplant and every dev on call suddenly became a philosopher, asking deep questions like, “What even is uptime?”
Meanwhile, Twitter (sorry, X) was flooded with the usual suspects: screenshots of outage maps, memes of panicked sysadmins, and devs pretending to “monitor status pages” while secretly refreshing DownDetector like it’s the stock market.
If you’ve worked in tech long enough, you’ve seen this show before. But every time it happens, it feels like a cosmic reminder that the internet isn’t as invincible as we pretend it is. We build layers on top of layers of “redundancy,” but one slip one auth service, one DNS glitch, one misconfigured rollout and poof, productivity across the planet drops faster than your Wi-Fi when the microwave turns on.
TL;DR: Even the biggest clouds fall. This isn’t about AWS vs. Azure it’s about what happens when the internet’s backbone starts to wobble, and what that teaches us about fragility, over-centralization, and survival as developers in the age of “always online.”
The illusion of infinite uptime
Every cloud provider loves to brag about “five nines.”
That magical 99.999% uptime which sounds like divine reliability until you realize it still allows for over 5 minutes of downtime per year. And that’s just per service. Stack a dozen of them together in your architecture, and suddenly you’re betting your app’s health on a cosmic game of Jenga.
AWS, Azure, GCP they all sell the dream of infinite uptime. Dashboards full of green dots, latency charts that look like tranquil heartbeats. But behind that marketing bliss is a cold truth every engineer learns eventually: uptime isn’t measured in seconds it’s measured in dependencies.
If your app relies on S3, Route 53, Lambda, CloudFront, and DynamoDB… congratulations, you’re already juggling five possible points of failure.
I still remember bragging to my PM about our “self-healing” infra one Friday afternoon right before AWS Route 53 went down. Our alerts lit up like a Christmas tree, the dashboards went blank, and my so-called self-healing system healed nothing except my inflated ego.
Even worse, the service status pages stayed green for the first 20 minutes, like they were gaslighting us. That’s when I learned the real SLA: “You’ll know it’s bad when Reddit knows it’s bad.”
The illusion of infinite uptime doesn’t come from the tech it comes from how much we want to believe in it. Because trusting the cloud means less overhead, less worry, fewer sleepless nights… until the night the lights actually go out.
When the cloud falls, everything burns
Here’s the fun part about modern cloud infra: when one piece breaks, everything else politely panics.
When Azure went down, it wasn’t just Azure. It was Microsoft 365, Teams, Outlook, OneDrive basically every service that makes corporate life tolerable (or at least bearable). The issue? A global authentication failure. One service forgot who everyone was, and suddenly the entire world got locked out of work like it was Monday morning and we all collectively forgot our passwords.
You could practically feel the ripple effect across the internet. Devs rushing to Slack, realizing Slack is also down, jumping to Discord, realizing Discord’s status page is also hosted on Azure. It was like a digital fire drill where every exit led back into the burning building.
I was in the middle of a deploy when it happened. Our CI/CD pipeline froze mid-run, the build logs stopped updating, and for a brief moment I thought, “Did I break prod?” (the most terrifying sentence in engineering). Then the group chat lit up with the classic line:
“Is it us or them?”
That’s the dev ops equivalent of “Are we the baddies?” and the answer, thankfully, was “them.” This time.
The irony is that cloud outages are often caused by the same thing we praise clouds for: automation. One wrong update to a control plane, one misconfigured rollout in a global network, and suddenly millions of servers all simultaneously agree to stop working. It’s poetic, in a tragic way.
Reddit’s r/sysadmin threads became therapy sessions. “We’re just watching dashboards cry,” one post read. Another dev shared a screenshot captioned, “Azure status: yellow. My soul: red.”
So yeah when the cloud falls, it’s not just an outage. It’s a global reminder that the internet is less like a spaceship and more like a stack of Jenga blocks we keep insisting is “resilient.”
Redundancy isn’t what you think it is
“Don’t worry,” they said. “We have multi-AZ redundancy.”
Cool so when one region dies, you’ve got… another region that uses the same control plane, same IAM layer, same global DNS, and the same misconfigured update that caused the first crash.
That’s like saying your parachute has a backup packed by the same guy who fell asleep during the safety briefing.
Every dev who’s ever designed for “high availability” has eventually learned that redundancy isn’t about copies, it’s about isolation. Real redundancy means independence. The problem? Most cloud services aren’t truly independent they just pretend to be.
When AWS or Azure talk about “multiple availability zones,” they don’t mean completely separate systems. They mean separate data centers sharing a ton of upstream services identity, DNS, APIs, telemetry, and global routing. If any of those layers fail, all your precious replicas, load balancers, and region mirrors are just standing around, waiting for the same broken dependency to recover.
I learned this the hard way when an internal tool we hosted in “two regions” went down during a DNS outage. Both regions. At once. Same bug, same TTL, same helpless Slack thread of engineers pretending to “triage.”
One of us finally said, “Wait, aren’t we supposed to fail over?”
Someone else replied, “We did. We failed over to the other failure.”
Redundancy gives comfort, not immunity. It’s a psychological patch a checkbox in a risk doc that helps everyone sleep at night until reality kicks in.
Here’s the plot twist: the real single point of failure isn’t the cloud it’s trust. We offload complexity because it’s convenient, and then we’re surprised when the convenience fails us.
The multi-cloud myth
Ah yes the classic boardroom solution: “We’ll just go multi-cloud!”
Every architect says it at least once. It sounds smart, future-proof, and heroic like shouting, “We shall never be vendor-locked again!” But then the engineers actually have to make it work… and reality hits harder than an unhandled exception in production.
On paper, multi-cloud promises freedom. You’re not tied to one provider, you can route around outages, and you’re “maximizing resilience.” In practice, it feels like trying to date AWS and Azure at the same time both want exclusive commitment, both charge you for attention, and neither likes that you’re seeing the other.
Every service has its own quirks. IAM roles in AWS? Totally different from Azure AD permissions. Networking, billing, SDKs, even CLI behavior nothing lines up. We once tried running part of our pipeline on GCP for “redundancy,” and ended up with Terraform configs so complex they could qualify as modern art. It wasn’t multi-cloud it was multi-chaos.
And here’s the dirty secret: most companies saying “multi-cloud” are actually doing “multi-region.” They just rebranded it for investor slides. Because true multi-cloud means duplicate infra, toolchains, CI/CD, monitoring, secrets, and staff trained in both ecosystems. That’s not cheap or fun.
My favorite Hacker News comment on the topic said:
“Multi-cloud is like installing two parachutes that both open when the plane takes off.”
And honestly? That sums it up perfectly.
So when AWS or Azure collapses, multi-cloud advocates come crawling out with “I told you so” energy. But ask them if their system is actually running in both not could, but is and watch the silence spread faster than an S3 bucket misconfiguration.
Dev lessons from chaos
Every outage leaves behind two kinds of engineers: the ones who panic, and the ones who quietly open five status pages and start brewing coffee. If you’ve survived a few of these “internet earthquakes,” you start developing instincts not disaster recovery plans, but emotional resilience.
Here’s the first lesson: never trust the status page. By the time it turns orange or red, you’ve already been offline for 20 minutes. Bookmark DownDetector, Reddit’s r/sysadmin, and your favorite cloud’s Twitter bot they’re faster, funnier, and way more honest.
Second: don’t make one cloud’s identity your single point of failure. If your internal tools, CI/CD, and monitoring all depend on the same provider’s login, congratulations you’ve invented a global kill switch. Use local accounts, backup credentials, and at least one self-hosted dashboard that works even when the world doesn’t.
Third: local failover still matters. Spinning up a tiny on-prem node for your most critical metrics might sound old-school, but during one outage, my dusty ThinkPad running Prometheus became our entire observability stack. It didn’t scale but it didn’t need to. It just worked.
Finally: practice chaos. Not in life (we already have that), but in infra. Tools like Gremlin or Netflix’s Chaos Monkey are worth their weight in uptime. They teach your team how to fail gracefully before the universe does it for you.
At the end of the day, every outage is a stress test not just of your infrastructure, but of your assumptions. The teams that recover fastest aren’t the ones with the most servers… they’re the ones who already expected the crash.
What this means for the future
The cloud isn’t dying but our blind faith in it probably should.
These outages are like nature’s way of reminding us that centralization always comes with a cost. We built an internet that’s faster, cheaper, and “infinitely scalable,” but also more fragile than we’d like to admit. When a single Azure region sneezes, half the world catches the flu.
The next evolution isn’t about ditching the cloud it’s about diversifying it.
Edge computing, hybrid deployments, containerized everything these aren’t buzzwords anymore, they’re self-defense. Maybe your next project runs its front-end on Vercel, backend on Fly.io, and backups on a Raspberry Pi in your closet (don’t laugh that Pi will probably outlast your region).
Developers are slowly rediscovering something we lost: ownership. The new wave isn’t anti-cloud; it’s post-cloud. We’ll still use AWS and Azure, but we’ll treat them like frameworks, not foundations use what works, self-host the critical bits, and design for failure from day one.
Because serverless doesn’t mean “outage-less.” And multi-cloud doesn’t mean “bulletproof.” The real future belongs to devs who understand trade-offs, not just tools.
So maybe, just maybe, next time the internet goes down, we’ll handle it better not with panic, but with popcorn and a well-documented fallback plan.
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