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Understand What You're Paying For: How to Evaluate Software

Dirk Paessler

The technology landscape is littered with confusing terminology. Some of this comes from vendors chasing popular buzzwords, other times it's the fault of a 30,000-foot view approach to different categories.

The term "monitoring," for example, can mean any number of things, and while more specified terms like application performance monitoring, network performance monitoring, or infrastructure monitoring are supposed to narrow it down, there is often overlap and confusion into what is supposed to go where. This is common across many IT categories, especially once we involve major buzzwords like cloud or software-defined.

Compounding the confusion is the changing nature of software sales, maintenance and operation, with the addition of new delivery models, licensing models and service-level agreements. An IT administrator may have simple goals in mind, but they will have to navigate an increasingly complex world to accomplish them. With that in mind, here are several key areas to focus on when evaluating your next IT purchase.

Licensing

Purchasing software may seem like a simple task, but there are often unexpected hurdles, the first of which is licensing and payment models. The growth of the "as a service" model has displaced many traditional "pay upfront" models, but it's important to understand whether the software purchased is all-inclusive.

Many products on the market are made up of various components, for which numerous modules and add-ons are available. It is difficult to determine just what will actually be necessary in terms of additional software before you buy, and what's worse, there is often little clarity offered on behalf of the seller. Before you buy, be sure to understand exactly what is needed in a product feature set, and match that up with associated costs to do a true price evaluation.

Evaluation and Testing

In a perfect world, every software can be evaluated and tested with a full-featured trial version. That may not always be the case, and that needs to be considered when making any purchase. IT administrators need easy access to trials, technical papers, data sheets and other information, along with dedicated assistance from the vendor should they run into any problems during evaluation. That's a must, and if vendors don't offer it, that should stand out as a red flag.

During the evaluation phase, it's also important to take note of the implementation process. If there are numerous problems with installing and configuring a trial version of the software, it can almost be guaranteed that the full version will be even more difficult.

Implementation and Usability

Ideally, the evaluation phase is a good indicator of how successful implementation will be. Still, it's key to fully comprehend all the challenges that can come from a complete implementation, many of which can undermine the functionality of the product. In the network monitoring world, this is often where the delivery model of the software comes in, with SaaS models often having different outcomes than appliance models in terms of installation and configuration. Implementations that aren't lightweight and automatic create more opportunities for something to go wrong, and problems may not be immediately apparent.

Usability itself is difficult to vet, as one can't understand the full value of any software until they use it. Here, it's important to trust peer networks and dive into case studies and customer references. The media can play a valuable role here as well, including news outlets that still publish reviews.

Ultimately, software that goes unused is a massive loss in terms of both money and potential technical gains. Keeping these issues in mind can ensure a smooth and simple software acquisition process, one that will enable IT to be successful with the right tools at their side.

Dirk Paessler is CEO and Founder of Paessler AG.

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 ...

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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.

Understand What You're Paying For: How to Evaluate Software

Dirk Paessler

The technology landscape is littered with confusing terminology. Some of this comes from vendors chasing popular buzzwords, other times it's the fault of a 30,000-foot view approach to different categories.

The term "monitoring," for example, can mean any number of things, and while more specified terms like application performance monitoring, network performance monitoring, or infrastructure monitoring are supposed to narrow it down, there is often overlap and confusion into what is supposed to go where. This is common across many IT categories, especially once we involve major buzzwords like cloud or software-defined.

Compounding the confusion is the changing nature of software sales, maintenance and operation, with the addition of new delivery models, licensing models and service-level agreements. An IT administrator may have simple goals in mind, but they will have to navigate an increasingly complex world to accomplish them. With that in mind, here are several key areas to focus on when evaluating your next IT purchase.

Licensing

Purchasing software may seem like a simple task, but there are often unexpected hurdles, the first of which is licensing and payment models. The growth of the "as a service" model has displaced many traditional "pay upfront" models, but it's important to understand whether the software purchased is all-inclusive.

Many products on the market are made up of various components, for which numerous modules and add-ons are available. It is difficult to determine just what will actually be necessary in terms of additional software before you buy, and what's worse, there is often little clarity offered on behalf of the seller. Before you buy, be sure to understand exactly what is needed in a product feature set, and match that up with associated costs to do a true price evaluation.

Evaluation and Testing

In a perfect world, every software can be evaluated and tested with a full-featured trial version. That may not always be the case, and that needs to be considered when making any purchase. IT administrators need easy access to trials, technical papers, data sheets and other information, along with dedicated assistance from the vendor should they run into any problems during evaluation. That's a must, and if vendors don't offer it, that should stand out as a red flag.

During the evaluation phase, it's also important to take note of the implementation process. If there are numerous problems with installing and configuring a trial version of the software, it can almost be guaranteed that the full version will be even more difficult.

Implementation and Usability

Ideally, the evaluation phase is a good indicator of how successful implementation will be. Still, it's key to fully comprehend all the challenges that can come from a complete implementation, many of which can undermine the functionality of the product. In the network monitoring world, this is often where the delivery model of the software comes in, with SaaS models often having different outcomes than appliance models in terms of installation and configuration. Implementations that aren't lightweight and automatic create more opportunities for something to go wrong, and problems may not be immediately apparent.

Usability itself is difficult to vet, as one can't understand the full value of any software until they use it. Here, it's important to trust peer networks and dive into case studies and customer references. The media can play a valuable role here as well, including news outlets that still publish reviews.

Ultimately, software that goes unused is a massive loss in terms of both money and potential technical gains. Keeping these issues in mind can ensure a smooth and simple software acquisition process, one that will enable IT to be successful with the right tools at their side.

Dirk Paessler is CEO and Founder of Paessler AG.

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.