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Avoiding Cost Traps in Cloud Monitoring

Martin Hirschvogel
Checkmk

Choosing the right approach is critical with cloud monitoring in hybrid environments. Otherwise, you may drive up costs with features you don’t need and risk diminishing the visibility of your on-premises IT.

The complexity of IT infrastructures is constantly growing as organizations continue to combine cloud-based services with on-premises or edge IT infrastructure and adopt Kubernetes or serverless computing services. To ensure that their hybrid IT infrastructure performs optimally, ITOps teams need a monitoring solution that is capable of providing comprehensive visibility while easing their burden.

Different Monitoring Requirements

To avoid blind spots and budget bloat, there are two main questions ITOps needs to consider:

What applications and resources do we run in which part of the infrastructure?

And what monitoring requirements result from this?

This is especially important when considering cloud monitoring solutions. While they provide numerous functions for monitoring applications and computing resources residing in the cloud, they have limitations when it comes to monitoring on-premises environments. So, by operating all their business-critical IT assets locally and "only" virtual machines in the cloud, organizations would risk driving up expenses and impacting IT operations by implementing cloud monitoring.

NonTransparent Pricing Models

Even if an organization is running mission-critical workloads in the cloud, choosing a cloud monitoring solution can quickly result in costs that are unexpected, but ultimately avoidable. This is due to cloud monitoring providers' sometimes opaque billing models that impose a kind of penalty tax on the benefits of the cloud, such as flexibility and scalability. When you add subscriptions for additional features to the high base fee for the software, the initial cost quickly becomes unmanageable.

A virtual server in a popular configuration costs about $100 per month from a hyperscaler. Basic monitoring for such a host typically starts at $15 to $30 from cloud monitoring providers, and the cost can be many times higher depending on the desired feature set and sizing. Even simple monitoring of the operating system can quickly add up to at least 30 percent of the hosting bill.

Expensive Host-Based Billing

Host-based billing may seem simple at first glance. Yet the question arises as to whether host-based billing makes sense at all in a serverless world with managed services, etc., where hosts no longer play a major role.

Also, in a serverless world with managed services from cloud providers, it is difficult to quantify hosts. In the end, this will inevitably lead to the gradual introduction of secondary pricing metrics and, from the user's perspective, to costs that are difficult to predict and a lack of price transparency.

The conceptual problems of host-based pricing are particularly evident in the fact that many monitoring providers have introduced limits and additional price dimensions. For example, in some cases only a certain number of containers per host are included in cloud monitoring. However, this limit is usually quickly exceeded and additional fees apply for each additional container.

Artificial Limits and Custom Metrics

Custom metrics, which allow special data to be included in monitoring, can also quickly drive up costs. This is especially the case if custom metrics are essential for monitoring and you can only obtain useful monitoring by adding them. Artificial currencies or units in monitoring, such as those used to retrieve custom metrics, logs, or user-defined events, and which have complex conversion formulas, also do not necessarily provide a transparent view of costs.

Monitoring costs also vary depending on the cloud provider. For example, with a hyperscaler, all of the API calls that are required to monitor the cloud services cost money. With another provider, the API calls may be free, but you may run into rate limits. These are all cost factors that should be taken into account from the outset when choosing a monitoring solution.

Evaluating a cloud monitoring solution also includes ensuring that the solution supports all of the necessary features and services. Essential features, such as an SSO solution based on the SAML standard, should not be reserved for the higher-tier product and the associated more expensive plan levels.

Wrong Incentives and Exclusive Access

The pricing model of a good monitoring solution should also not create incentives to compromise on infrastructure architecture for cost reasons. For example, if an organization has to pay per monitoring instance, there is a strong temptation to save costs by minimizing the number of instances. However, there is a risk that the monitoring will not scale with the company's infrastructure — negating a key benefit of the cloud.

The goal of IT monitoring is to provide critical insight into IT infrastructure health and performance. Access to monitoring is critical for various teams to gain important insights for their daily work and to ensure smooth IT operations. However, charging on a per-user basis for monitoring could result in this information being made available only to an exclusive group to keep costs down. As a result, responsible individuals and teams would be denied visibility into the IT assets that are important to them, and the monitoring would be of no value to them.

Avoiding Cost Traps

A look at the market shows that the pricing of many monitoring vendors can quickly blow the monitoring budget due to hidden costs or subsequent price drivers — or even encourage the creation of poor IT architectures. If you are not careful, you can quickly end up paying 30 percent of your computing costs for monitoring. For comparison, common benchmarks suggest that ITOps should spend no more than 3 to 15 percent of its IT budget on observability, depending on the industry and the size of the organization.

Organizations should develop clear strategies and understand which business areas are running and will run on which parts of their IT architecture. Only by understanding your cloud and on-premises monitoring needs can you find a tailored solution with a precise and predictable pricing model, rather than paying a lot of money for an oversized solution that may not fit your infrastructure.

Martin Hirschvogel is Chief Product Officer at Checkmk

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Avoiding Cost Traps in Cloud Monitoring

Martin Hirschvogel
Checkmk

Choosing the right approach is critical with cloud monitoring in hybrid environments. Otherwise, you may drive up costs with features you don’t need and risk diminishing the visibility of your on-premises IT.

The complexity of IT infrastructures is constantly growing as organizations continue to combine cloud-based services with on-premises or edge IT infrastructure and adopt Kubernetes or serverless computing services. To ensure that their hybrid IT infrastructure performs optimally, ITOps teams need a monitoring solution that is capable of providing comprehensive visibility while easing their burden.

Different Monitoring Requirements

To avoid blind spots and budget bloat, there are two main questions ITOps needs to consider:

What applications and resources do we run in which part of the infrastructure?

And what monitoring requirements result from this?

This is especially important when considering cloud monitoring solutions. While they provide numerous functions for monitoring applications and computing resources residing in the cloud, they have limitations when it comes to monitoring on-premises environments. So, by operating all their business-critical IT assets locally and "only" virtual machines in the cloud, organizations would risk driving up expenses and impacting IT operations by implementing cloud monitoring.

NonTransparent Pricing Models

Even if an organization is running mission-critical workloads in the cloud, choosing a cloud monitoring solution can quickly result in costs that are unexpected, but ultimately avoidable. This is due to cloud monitoring providers' sometimes opaque billing models that impose a kind of penalty tax on the benefits of the cloud, such as flexibility and scalability. When you add subscriptions for additional features to the high base fee for the software, the initial cost quickly becomes unmanageable.

A virtual server in a popular configuration costs about $100 per month from a hyperscaler. Basic monitoring for such a host typically starts at $15 to $30 from cloud monitoring providers, and the cost can be many times higher depending on the desired feature set and sizing. Even simple monitoring of the operating system can quickly add up to at least 30 percent of the hosting bill.

Expensive Host-Based Billing

Host-based billing may seem simple at first glance. Yet the question arises as to whether host-based billing makes sense at all in a serverless world with managed services, etc., where hosts no longer play a major role.

Also, in a serverless world with managed services from cloud providers, it is difficult to quantify hosts. In the end, this will inevitably lead to the gradual introduction of secondary pricing metrics and, from the user's perspective, to costs that are difficult to predict and a lack of price transparency.

The conceptual problems of host-based pricing are particularly evident in the fact that many monitoring providers have introduced limits and additional price dimensions. For example, in some cases only a certain number of containers per host are included in cloud monitoring. However, this limit is usually quickly exceeded and additional fees apply for each additional container.

Artificial Limits and Custom Metrics

Custom metrics, which allow special data to be included in monitoring, can also quickly drive up costs. This is especially the case if custom metrics are essential for monitoring and you can only obtain useful monitoring by adding them. Artificial currencies or units in monitoring, such as those used to retrieve custom metrics, logs, or user-defined events, and which have complex conversion formulas, also do not necessarily provide a transparent view of costs.

Monitoring costs also vary depending on the cloud provider. For example, with a hyperscaler, all of the API calls that are required to monitor the cloud services cost money. With another provider, the API calls may be free, but you may run into rate limits. These are all cost factors that should be taken into account from the outset when choosing a monitoring solution.

Evaluating a cloud monitoring solution also includes ensuring that the solution supports all of the necessary features and services. Essential features, such as an SSO solution based on the SAML standard, should not be reserved for the higher-tier product and the associated more expensive plan levels.

Wrong Incentives and Exclusive Access

The pricing model of a good monitoring solution should also not create incentives to compromise on infrastructure architecture for cost reasons. For example, if an organization has to pay per monitoring instance, there is a strong temptation to save costs by minimizing the number of instances. However, there is a risk that the monitoring will not scale with the company's infrastructure — negating a key benefit of the cloud.

The goal of IT monitoring is to provide critical insight into IT infrastructure health and performance. Access to monitoring is critical for various teams to gain important insights for their daily work and to ensure smooth IT operations. However, charging on a per-user basis for monitoring could result in this information being made available only to an exclusive group to keep costs down. As a result, responsible individuals and teams would be denied visibility into the IT assets that are important to them, and the monitoring would be of no value to them.

Avoiding Cost Traps

A look at the market shows that the pricing of many monitoring vendors can quickly blow the monitoring budget due to hidden costs or subsequent price drivers — or even encourage the creation of poor IT architectures. If you are not careful, you can quickly end up paying 30 percent of your computing costs for monitoring. For comparison, common benchmarks suggest that ITOps should spend no more than 3 to 15 percent of its IT budget on observability, depending on the industry and the size of the organization.

Organizations should develop clear strategies and understand which business areas are running and will run on which parts of their IT architecture. Only by understanding your cloud and on-premises monitoring needs can you find a tailored solution with a precise and predictable pricing model, rather than paying a lot of money for an oversized solution that may not fit your infrastructure.

Martin Hirschvogel is Chief Product Officer at Checkmk

Hot Topics

The Latest

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 ...

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.