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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

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In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

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

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...