Skip to main content

Understand Your Infrastructure's Usage and Load Characteristics for Online Success

Sven Hammar

Cyber Monday is here and it is instrumental to keep your e-commerce platform in perfect health. Key areas to keep an eye on are usage and load characteristics — they are gauges for interpreting how much work an online platform performs, and how well it performs under stress.

Understanding how your digital infrastructure performs is not as simple as assessing your car’s odometer to measure distance traveled, or its speedometer to measure the maximum speed. Usage and load characteristics provide insight into the performance of the platform in real-world use cases, like analyzing that metaphorical car’s journey from point A to point B.

Understanding what kind of usage and load capacity your service concurrently supports, as well as changes to those factors in the future, is a vital part of providing an excellent online user experience. Cloud computing and scaling services are great assets because you have almost unlimited server resources to handle traffic spikes and growth; however, your service may suffer if you are not configured to use it.

On the other side, you do not want to be paying for more power than you need. Use your interpretation of usage and load characteristics to know your limits, check up on the user experience, and evaluate poor performance issues.

Usage: How Much Are You Utilizing?

Usage characteristics are a practical way to measure how much server power you need to run your web or mobile platform. Your usage characteristics are going to break down into CPU, memory, storage, pageviews and network load statistics which can be measured over time or by time increments. The usage data sheds light on how much information your platform is moving to end users, as well as when it moves.

Usage can also tell you how many users are accessing your service at a specific time and compare that against usage statistics to see how hard they are pushing the system. An example usage characteristic would be your web application moving 100GB of data within a month and 10,000 pageviews per hour.

Load: Can You Take the Heat?

Load characteristics can tell you how well your platform performs depending on how many end users are accessing the service concurrently, as well as the maximum amount of work the service can handle before it starts to experience performance problems. Whereas usage testing identifies how much information moves, load testing examines how efficiently the service moves that information.

Load testing, whether performed during development or on a live, fully functioning application, is like test-driving the user experience to make sure everything runs smoothly on a larger scale.

Using load testing analytics, you can identify capacity shortcomings and single out bottleneck points where the platform or server instances can be improved. Load testing gauges how well a platform holds up in terms of service capacity, long-term high use endurance conditions, and demand spikes. It is great for identifying problems with latency as well — something usage data does not provide any insight into. An example load characteristic is the latency between users when a typical number is simultaneously using the service.

Combining Both for Hosting Capacity and Programming Efficiency Analysis

Looking at your web service’s usage and load characteristics helps answer the question of whether your platform needs to make programming efficiency improvements and adjust hosting resources.

If your service passes the test with little headroom, it is an indication that future growth will disrupt service quality. The performance data helps businesses avoid being victims of their own success. Unpredictable load and rapid use expansion can cause the service to falter if the hosting services are not prepared.

For example, when Pinterest first launched, they used a gated account approval method at first for gradually allowing new users to access the service. This prevented them from overloading the application and creating a poor user experience.

Take advantage of the information that usage and load characteristics provide, adjusting service capabilities and your auto scaling settings to address problems with real-world service use. Do not become a victim of your own online success!

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 ...

Understand Your Infrastructure's Usage and Load Characteristics for Online Success

Sven Hammar

Cyber Monday is here and it is instrumental to keep your e-commerce platform in perfect health. Key areas to keep an eye on are usage and load characteristics — they are gauges for interpreting how much work an online platform performs, and how well it performs under stress.

Understanding how your digital infrastructure performs is not as simple as assessing your car’s odometer to measure distance traveled, or its speedometer to measure the maximum speed. Usage and load characteristics provide insight into the performance of the platform in real-world use cases, like analyzing that metaphorical car’s journey from point A to point B.

Understanding what kind of usage and load capacity your service concurrently supports, as well as changes to those factors in the future, is a vital part of providing an excellent online user experience. Cloud computing and scaling services are great assets because you have almost unlimited server resources to handle traffic spikes and growth; however, your service may suffer if you are not configured to use it.

On the other side, you do not want to be paying for more power than you need. Use your interpretation of usage and load characteristics to know your limits, check up on the user experience, and evaluate poor performance issues.

Usage: How Much Are You Utilizing?

Usage characteristics are a practical way to measure how much server power you need to run your web or mobile platform. Your usage characteristics are going to break down into CPU, memory, storage, pageviews and network load statistics which can be measured over time or by time increments. The usage data sheds light on how much information your platform is moving to end users, as well as when it moves.

Usage can also tell you how many users are accessing your service at a specific time and compare that against usage statistics to see how hard they are pushing the system. An example usage characteristic would be your web application moving 100GB of data within a month and 10,000 pageviews per hour.

Load: Can You Take the Heat?

Load characteristics can tell you how well your platform performs depending on how many end users are accessing the service concurrently, as well as the maximum amount of work the service can handle before it starts to experience performance problems. Whereas usage testing identifies how much information moves, load testing examines how efficiently the service moves that information.

Load testing, whether performed during development or on a live, fully functioning application, is like test-driving the user experience to make sure everything runs smoothly on a larger scale.

Using load testing analytics, you can identify capacity shortcomings and single out bottleneck points where the platform or server instances can be improved. Load testing gauges how well a platform holds up in terms of service capacity, long-term high use endurance conditions, and demand spikes. It is great for identifying problems with latency as well — something usage data does not provide any insight into. An example load characteristic is the latency between users when a typical number is simultaneously using the service.

Combining Both for Hosting Capacity and Programming Efficiency Analysis

Looking at your web service’s usage and load characteristics helps answer the question of whether your platform needs to make programming efficiency improvements and adjust hosting resources.

If your service passes the test with little headroom, it is an indication that future growth will disrupt service quality. The performance data helps businesses avoid being victims of their own success. Unpredictable load and rapid use expansion can cause the service to falter if the hosting services are not prepared.

For example, when Pinterest first launched, they used a gated account approval method at first for gradually allowing new users to access the service. This prevented them from overloading the application and creating a poor user experience.

Take advantage of the information that usage and load characteristics provide, adjusting service capabilities and your auto scaling settings to address problems with real-world service use. Do not become a victim of your own online success!

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 ...