Skip to main content

After Amazon: 5 Ways BSM Can Protect You from the Next Cloud Outage

The Amazon cloud outage is a wake-up call for IT staff that are not adequately prepared for the journey to the cloud. Planning for migration of applications to any type of cloud – public or private, on-premise or off-premise – requires appropriate service management processes and infrastructure. Otherwise, you risk being unable to manage, or even understand, the business impact of future cloud outages.

When talking about business services in the cloud, it’s almost impossible to avoid the obvious play on words: when you move to the cloud, you lose visibility. In order to meet SLAs, maintain a quality user experience, and resolve problems quickly, you need a clear picture of your services as they traverse each hop of the infrastructure. But in the cloud, where resources are virtualized and allocated dynamically, you often have little idea where services are running.

The Amazon cloud outage demonstrates the point. When the outage occurred, the EC2 dashboard could not tell customers how their applications and services were performing. It did not provide round-trip transaction times or report on the user experience. Instead, it reported various problems with latency and errors that were eventually linked to the cloud storage service. Those KPIs did not tell EC2 customers how the outage was affecting their business. In fact, according to Amazon, the outage was not even a violation of customer SLAs – even though many sites went down completely.

Cloud computing requires a sophisticated approach to Business Service Management that enables you to track services from the data center and into the cloud. This post looks at 5 key capabilities that organizations must have in order to maintain visibility and control in the cloud:

1. Integrated, End-to-End Service View

In the cloud more than ever, you need a top-down view of your business services, end-to-end. The service cannot be a block box; instead, you need a topological map that shows the execution of the each service – also called a business transaction – as it traverses every server in the private and public cloud. As we saw last week, it is critical to build redundancy and not to rely on a single cloud provider for all of your needs, so you need a solution that can track complex hybrid architectures, even between clouds.

You need to see the performance not only round-trip, but on each leg of the journey. This is the only way to assure SLAs on the one hand, and to quickly identify the source of performance degradation on the other. Ideally, your solution will also provide some deep-dive capabilities so that in addition to identifying the problem tier, it will also lead you to the source of the problem.

2. Dynamic Service Discovery

Since dynamic resource allocation is a cornerstone of the cloud ROI model, the path of a service or transaction in the cloud will be changing. If your monitoring solution requires manual definition of services, it is very likely that it will not work properly in this type of environment.

To ensure accuracy and to save valuable time, it is important to choose a solution that automatically identifies business services and maintains a dynamic picture of service delivery.

3. Real End-User Experience Monitoring

Once of the most important indicators of application health is the experience of real end-users. Synthetic transactions can provide an important indicator during quiet times but they cannot tell you what all of your users are experiencing, all of the time. Setting up a real-user monitoring solution in the cloud can be complicated since you do not necessarily control the point on the network between the application and your users. You should make sure that your monitoring solution can track real-user transactions in any cloud configuration. This is a crucial piece of information that puts the technical information from your cloud services provider into business context.

4. Change Management

Even in the datacenter, change is probably the greatest risk to service stability. That risk is magnified exponentially in the cloud where any change to code, hardware, or configuration can affect the behavior and performance of business services in unpredictable ways. Again, the Amazon outage shows us that even in the cloud, you may have to make some fast decisions and changes in order to keep your critical services on line.

To mitigate the danger, you need a monitoring solution that can baseline service performance and analyze the impact of change on a wide variety of parameters. It’s important to choose a solution that captures all transaction instances – and does not rely on sampling – so that you can accurately analyze problems and find root causes that occurred before a service level alarm would have been triggered.

5. Effective Communications

One of the biggest obstacles to the cloud is the – understandable – fear of business owners that performance and usability will decline. Many application owners are concerned about the risks of sharing resources and are reluctant to accept the standardization and loss of control inherent in the cloud model. Unfortunately, well-publicized events such as the Amazon outage will only exacerbate those fears.

Yet the benefits of the cloud are real, and IT must be able to not only mitigate the risks of outages, but also to demonstrate the benefits to a business audience. You need a solution that measures performance and user experience, and can communicate them in a robust and intuitive fashion.

Russell Rothstein is Founder and CEO, IT Central Station.

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 ...

After Amazon: 5 Ways BSM Can Protect You from the Next Cloud Outage

The Amazon cloud outage is a wake-up call for IT staff that are not adequately prepared for the journey to the cloud. Planning for migration of applications to any type of cloud – public or private, on-premise or off-premise – requires appropriate service management processes and infrastructure. Otherwise, you risk being unable to manage, or even understand, the business impact of future cloud outages.

When talking about business services in the cloud, it’s almost impossible to avoid the obvious play on words: when you move to the cloud, you lose visibility. In order to meet SLAs, maintain a quality user experience, and resolve problems quickly, you need a clear picture of your services as they traverse each hop of the infrastructure. But in the cloud, where resources are virtualized and allocated dynamically, you often have little idea where services are running.

The Amazon cloud outage demonstrates the point. When the outage occurred, the EC2 dashboard could not tell customers how their applications and services were performing. It did not provide round-trip transaction times or report on the user experience. Instead, it reported various problems with latency and errors that were eventually linked to the cloud storage service. Those KPIs did not tell EC2 customers how the outage was affecting their business. In fact, according to Amazon, the outage was not even a violation of customer SLAs – even though many sites went down completely.

Cloud computing requires a sophisticated approach to Business Service Management that enables you to track services from the data center and into the cloud. This post looks at 5 key capabilities that organizations must have in order to maintain visibility and control in the cloud:

1. Integrated, End-to-End Service View

In the cloud more than ever, you need a top-down view of your business services, end-to-end. The service cannot be a block box; instead, you need a topological map that shows the execution of the each service – also called a business transaction – as it traverses every server in the private and public cloud. As we saw last week, it is critical to build redundancy and not to rely on a single cloud provider for all of your needs, so you need a solution that can track complex hybrid architectures, even between clouds.

You need to see the performance not only round-trip, but on each leg of the journey. This is the only way to assure SLAs on the one hand, and to quickly identify the source of performance degradation on the other. Ideally, your solution will also provide some deep-dive capabilities so that in addition to identifying the problem tier, it will also lead you to the source of the problem.

2. Dynamic Service Discovery

Since dynamic resource allocation is a cornerstone of the cloud ROI model, the path of a service or transaction in the cloud will be changing. If your monitoring solution requires manual definition of services, it is very likely that it will not work properly in this type of environment.

To ensure accuracy and to save valuable time, it is important to choose a solution that automatically identifies business services and maintains a dynamic picture of service delivery.

3. Real End-User Experience Monitoring

Once of the most important indicators of application health is the experience of real end-users. Synthetic transactions can provide an important indicator during quiet times but they cannot tell you what all of your users are experiencing, all of the time. Setting up a real-user monitoring solution in the cloud can be complicated since you do not necessarily control the point on the network between the application and your users. You should make sure that your monitoring solution can track real-user transactions in any cloud configuration. This is a crucial piece of information that puts the technical information from your cloud services provider into business context.

4. Change Management

Even in the datacenter, change is probably the greatest risk to service stability. That risk is magnified exponentially in the cloud where any change to code, hardware, or configuration can affect the behavior and performance of business services in unpredictable ways. Again, the Amazon outage shows us that even in the cloud, you may have to make some fast decisions and changes in order to keep your critical services on line.

To mitigate the danger, you need a monitoring solution that can baseline service performance and analyze the impact of change on a wide variety of parameters. It’s important to choose a solution that captures all transaction instances – and does not rely on sampling – so that you can accurately analyze problems and find root causes that occurred before a service level alarm would have been triggered.

5. Effective Communications

One of the biggest obstacles to the cloud is the – understandable – fear of business owners that performance and usability will decline. Many application owners are concerned about the risks of sharing resources and are reluctant to accept the standardization and loss of control inherent in the cloud model. Unfortunately, well-publicized events such as the Amazon outage will only exacerbate those fears.

Yet the benefits of the cloud are real, and IT must be able to not only mitigate the risks of outages, but also to demonstrate the benefits to a business audience. You need a solution that measures performance and user experience, and can communicate them in a robust and intuitive fashion.

Russell Rothstein is Founder and CEO, IT Central Station.

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 ...