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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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I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

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Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

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New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

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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

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

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 ...