Skip to main content

Hard Lessons of Public Cloud: Designing for When AWS Goes Down

Eric Wright

So, your site is down because AWS S3 went away. Too soon? It's never too soon to talk about why the responsibility for designing resilient infrastructure belongs in your camp. It's like when Smokey the Bear used to say that "only you can prevent forest fires." The difference is that it's Jeff Bezos saying it this time.

We have some real insight into what design for cloud resiliency really means thanks to a chat that I had recently.

Cloud Goes Down, so Design for It

There is no special text in the terms and conditions. These are hard facts. AWS designs its infrastructure to be as resilient as possible, but clearly tells you that you should design with the intention of surviving partial service outages. It isn't that AWS plans on being down a lot, but they have been hit by specific DDoS attacks, and also have had to reboot EC2 hosts in order to patch for security vulnerabilities.

At the time I was writing this, AWS S3 was fighting its way back to life in the US-east-1 Region. This means that there were multiple Availability Zones in the throes of recovery, and that potentially hundreds of thousands of web sites, and applications were experiencing issues retrieving objects from the widely used object storage platform.

So, how do we do this better? Let's ask someone who does design and see how the developers think about things. With that, I wanted to share a great discussion that I had with former Disney lead architect and current Principal Software Architect at Turbonomic, Steve Haines.

Q&A: Understanding the Developer's Reaction to the AWS Outage

EW: What does it mean to think about designing across regions inside the public cloud?

SH: Designing an application to run across multiple AWS regions is not a trivial task. While you can deploy stateless services or micro-services to multiple regions and then configure Route53 (Amazon's DNS Service) to point to Elastic Load Balancers (ELBs) in each region, that doesn't completely solve the problem.

First, it's crucial to consider the cost of redundancy. How many regions and how many availability zones (AZ) in each region do we want to deploy to? From historical outages, you're probably safe with two regions, but you do not want to keep a full copy of your application deployed in another region just for disaster recovery: you want to use it and distribute workloads across those regions!

For some use cases this will be easy, but for others you will need to design your application so that it is close to the resources it needs to access. If you design your application with failure in mind and to run in multiple regions then you can manage the cost because both regions will be running your workloads.

EW: That seems to be a bit of the cost of doing business for design and resiliency, but what is the impact below the presentation layers? It feels like that is the sort of "low hanging fruit" as we know it, but there is much more to the application architecture than that, right?

SH: Exactly! That leads to the next challenge: resources, such as databases and files. While AWS provides users multi-A to Z database replication free of charge for databases running behind RDS, users are still paying for storage, IOPS, etc. However, this model changes if a user wants to replicate across regions. For example, Oracle provides a product called GoldenGate for performing cross-region replication, which is a great tool but can significantly impact your IT budget.

Alternatively, you can consider one of Amazon's native offerings, Aurora, which supports cross- region replication out-of-the-box, but that needs to be a design decision you make when you're building or refactoring your application. And, if you store files in S3, be sure that you enable cross- region replication, it will cost you more, but it will ensure that files stored in one region will be available in the event of a regional outage.

EW: Sounds like we have already got some challenges in front of us with just porting our designs to cloud platforms, but when you're already leaning into the cloud as a first-class destination for your apps we have to already think about big outages. We do disaster recovery testing on-premises because that's something we can control. How do we do that type of testing out in the public cloud?

SH: Good question. It's important to remember that while designing an application to run in a cross-region capacity is one thing, having the confidence that it will work when you lose a region is another beast altogether!

This is where I'll defer to Netflix's practice of designing for failure and regularly testing failure scenarios. They have a "Simian Army" (https://github.com/Netflix/SimianArmy) that simulates various failure scenarios in production and ensures that everything continues to work. One of the members of the Simian Army is the Chaos Gorilla that regularly kills a region and ensures that Netflix continues to function, which is one of the reasons they were able to sustain the previous full region outage.

If you're serious about running across regions then you need to regularly validate that it works!

But maybe we should think bigger than cross-region – what if we could design across clouds for the ultimate protection?

EW: Thanks for the background and advice, Steve. Good food for thought for all of us in the IT industry. I'm sure there are a lot of people having this discussion in the coming weeks after the recent outage.

Eric Wright is Principal Solutions Engineer at Turbonomic.

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 ...

Hard Lessons of Public Cloud: Designing for When AWS Goes Down

Eric Wright

So, your site is down because AWS S3 went away. Too soon? It's never too soon to talk about why the responsibility for designing resilient infrastructure belongs in your camp. It's like when Smokey the Bear used to say that "only you can prevent forest fires." The difference is that it's Jeff Bezos saying it this time.

We have some real insight into what design for cloud resiliency really means thanks to a chat that I had recently.

Cloud Goes Down, so Design for It

There is no special text in the terms and conditions. These are hard facts. AWS designs its infrastructure to be as resilient as possible, but clearly tells you that you should design with the intention of surviving partial service outages. It isn't that AWS plans on being down a lot, but they have been hit by specific DDoS attacks, and also have had to reboot EC2 hosts in order to patch for security vulnerabilities.

At the time I was writing this, AWS S3 was fighting its way back to life in the US-east-1 Region. This means that there were multiple Availability Zones in the throes of recovery, and that potentially hundreds of thousands of web sites, and applications were experiencing issues retrieving objects from the widely used object storage platform.

So, how do we do this better? Let's ask someone who does design and see how the developers think about things. With that, I wanted to share a great discussion that I had with former Disney lead architect and current Principal Software Architect at Turbonomic, Steve Haines.

Q&A: Understanding the Developer's Reaction to the AWS Outage

EW: What does it mean to think about designing across regions inside the public cloud?

SH: Designing an application to run across multiple AWS regions is not a trivial task. While you can deploy stateless services or micro-services to multiple regions and then configure Route53 (Amazon's DNS Service) to point to Elastic Load Balancers (ELBs) in each region, that doesn't completely solve the problem.

First, it's crucial to consider the cost of redundancy. How many regions and how many availability zones (AZ) in each region do we want to deploy to? From historical outages, you're probably safe with two regions, but you do not want to keep a full copy of your application deployed in another region just for disaster recovery: you want to use it and distribute workloads across those regions!

For some use cases this will be easy, but for others you will need to design your application so that it is close to the resources it needs to access. If you design your application with failure in mind and to run in multiple regions then you can manage the cost because both regions will be running your workloads.

EW: That seems to be a bit of the cost of doing business for design and resiliency, but what is the impact below the presentation layers? It feels like that is the sort of "low hanging fruit" as we know it, but there is much more to the application architecture than that, right?

SH: Exactly! That leads to the next challenge: resources, such as databases and files. While AWS provides users multi-A to Z database replication free of charge for databases running behind RDS, users are still paying for storage, IOPS, etc. However, this model changes if a user wants to replicate across regions. For example, Oracle provides a product called GoldenGate for performing cross-region replication, which is a great tool but can significantly impact your IT budget.

Alternatively, you can consider one of Amazon's native offerings, Aurora, which supports cross- region replication out-of-the-box, but that needs to be a design decision you make when you're building or refactoring your application. And, if you store files in S3, be sure that you enable cross- region replication, it will cost you more, but it will ensure that files stored in one region will be available in the event of a regional outage.

EW: Sounds like we have already got some challenges in front of us with just porting our designs to cloud platforms, but when you're already leaning into the cloud as a first-class destination for your apps we have to already think about big outages. We do disaster recovery testing on-premises because that's something we can control. How do we do that type of testing out in the public cloud?

SH: Good question. It's important to remember that while designing an application to run in a cross-region capacity is one thing, having the confidence that it will work when you lose a region is another beast altogether!

This is where I'll defer to Netflix's practice of designing for failure and regularly testing failure scenarios. They have a "Simian Army" (https://github.com/Netflix/SimianArmy) that simulates various failure scenarios in production and ensures that everything continues to work. One of the members of the Simian Army is the Chaos Gorilla that regularly kills a region and ensures that Netflix continues to function, which is one of the reasons they were able to sustain the previous full region outage.

If you're serious about running across regions then you need to regularly validate that it works!

But maybe we should think bigger than cross-region – what if we could design across clouds for the ultimate protection?

EW: Thanks for the background and advice, Steve. Good food for thought for all of us in the IT industry. I'm sure there are a lot of people having this discussion in the coming weeks after the recent outage.

Eric Wright is Principal Solutions Engineer at Turbonomic.

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 ...