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4 Key Resources to Monitor in the Cloud

Good application performance monitoring in the cloud involves repeatedly monitoring and testing a few key areas that act differently in most cloud environments than they do in traditional situations. Tracking the resulting values over time allows you to track normal usage patterns and trends, and determine normal behavior for your provider's resources.

Valuable resources to monitor in the cloud include:

1. Network Latency

If your application depends on access to a network resource, like DNS for reverse lookup of domain names for example, then the application should regularly test this resource and your monitoring system should record its results in an easily visualized format. Also, the access time to the hosts application from both cloud and non-cloud locations should be checked and tracked. This will allow differential latency comparisons that will help reduce uncertainty about the root cause of slow response time. For instance, if the application is fast from within the cloud, and slow from without, is there a network issue on the cloud provider's Internet facing systems?

2. Cloud API Feature Availability

If your application is dynamic, and needs to use features of the Cloud vendor's API to function, you should script and test those functions to ensure they are available, and that they perform fast enough to meet your needs. Functions like instance launching, taking a volume snapshot, or adding a new volume to a running instance are good things to test periodically.

3. Virtualization Overhead

Differential monitoring of instances in the cloud versus instances on actual hardware can help you determine overall virtualization overhead for your application. Knowing the relative performance will help you size the instances you launch, and let you calculate the cost of operation on cloud infrastructure versus in-house. This makes cost-benefit analysis and cost-based justification for using cloud systems possible.

4. Configuration Tracking

So many of the failures experienced by computing infrastructures are the result of improperly managed configuration changes. The knowledge of the last time a configuration was changed becomes a critical piece of information in root cause analysis. At a minimum, the monitoring system should have a record of boot time (often associated with updates or other configuration changes) and ideally it will also have some indication of the nature of the change.

While moving to the cloud can be cost-effective in the abstract, as with any technology project it’s important to validate the assumptions you make when determining what to move, and what the cost savings actually end up to be.

About Roger Ruttiman

Roger Ruttiman, VP of Engineering & Quality at GroundWork, has 18 years of software development and leadership experience. Ruttiman is the lead architect responsible for product architecture, building and managing local and offshore teams. Before joining GroundWork, Ruttiman was a lead engineer at Advent Software in San Francisco, and at Autodesk in the US and Europe.

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4 Key Resources to Monitor in the Cloud

Good application performance monitoring in the cloud involves repeatedly monitoring and testing a few key areas that act differently in most cloud environments than they do in traditional situations. Tracking the resulting values over time allows you to track normal usage patterns and trends, and determine normal behavior for your provider's resources.

Valuable resources to monitor in the cloud include:

1. Network Latency

If your application depends on access to a network resource, like DNS for reverse lookup of domain names for example, then the application should regularly test this resource and your monitoring system should record its results in an easily visualized format. Also, the access time to the hosts application from both cloud and non-cloud locations should be checked and tracked. This will allow differential latency comparisons that will help reduce uncertainty about the root cause of slow response time. For instance, if the application is fast from within the cloud, and slow from without, is there a network issue on the cloud provider's Internet facing systems?

2. Cloud API Feature Availability

If your application is dynamic, and needs to use features of the Cloud vendor's API to function, you should script and test those functions to ensure they are available, and that they perform fast enough to meet your needs. Functions like instance launching, taking a volume snapshot, or adding a new volume to a running instance are good things to test periodically.

3. Virtualization Overhead

Differential monitoring of instances in the cloud versus instances on actual hardware can help you determine overall virtualization overhead for your application. Knowing the relative performance will help you size the instances you launch, and let you calculate the cost of operation on cloud infrastructure versus in-house. This makes cost-benefit analysis and cost-based justification for using cloud systems possible.

4. Configuration Tracking

So many of the failures experienced by computing infrastructures are the result of improperly managed configuration changes. The knowledge of the last time a configuration was changed becomes a critical piece of information in root cause analysis. At a minimum, the monitoring system should have a record of boot time (often associated with updates or other configuration changes) and ideally it will also have some indication of the nature of the change.

While moving to the cloud can be cost-effective in the abstract, as with any technology project it’s important to validate the assumptions you make when determining what to move, and what the cost savings actually end up to be.

About Roger Ruttiman

Roger Ruttiman, VP of Engineering & Quality at GroundWork, has 18 years of software development and leadership experience. Ruttiman is the lead architect responsible for product architecture, building and managing local and offshore teams. Before joining GroundWork, Ruttiman was a lead engineer at Advent Software in San Francisco, and at Autodesk in the US and Europe.

Hot Topics

The Latest

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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 ...