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Evaluating Commercial vs. Open Source APM

Chris Bloom

Choosing an application performance monitoring (APM) solution can be a daunting task. A quick Google search will show popular products, but there's also a long list of less-well-known open source products available, too. So how do you choose the right solution?

The advantages and disadvantages of open source software versus commercial software are part of an age-old debate. Which is better or worse? Which is more or less buggy? Which is faster or slower? And what about documentation and support, and a million other possible differences? The truth is that they have very little to do with whether the software is open source or not.

Cost and Support

Characteristics like support and cost are of course very important, and can vary greatly between the two. The cost of open source may be something you pay to ramp up on the learning curve, and to tweak the software yourself, or pay somebody else to do it for you.

Support of open source can be very good, if the solution becomes popular within the community. On the other hand, a commercial product may have very polished, organized documentation and well-established support. In other words, aside from the quality of the product itself, a big part of the decision to go with open source or not is your preference on when to invest money in the product and how to get support. With open source, you have a lot of flexibility about when and what you spend your money on.
 
With many open source projects there are the completely free options, usually posted on Github as source. There are also commercial components and support provided for a fee from a company supporting the project. This can make getting started reasonably inexpensive if the open source project's functionality satisfies your requirements. As your requirements change and grow, you can develop new functionality yourself, buy components from others, or just wait until somebody else does the work.

One of the biggest benefits of open source from a cost perspective is that once you have something that works for you, you don't have to keep paying for it annually, like you would for a commercial product. On the other hand, paying for a commercial product gives you a well-defined set of features and characteristics that you can count on, and somebody to call and complain to otherwise.

APM Performance: Speed and Scalability

 
Performance is another good example of a software characteristic that cannot be determined by open source or commercial availability. Performance for APM software is defined by speed and scalability, mostly on the back-end. Sure, the front-end UI must be fast, but the real question and challenge for this type of software is how much analysis it can do on network traffic or flow data.

For smaller networks, let's say 100Mbps or less, this is not much of an issue. But when you start to get above that, the overwhelming volume of packets and flows that must be processed every second exceeds the limits of a single thread. This is where you need to consider whether the solution is multi-threaded or not.

And for networks with speeds in the 10Gbps arena and up, even multi-threaded software on a single server is not going to be enough. In this case, the solution needs to scale by distributing the load across several servers, and aggregating the results into a single pane of glass. In my own experience, I have found open source solutions to be more scalable than commercial products, or at least accessible to more people, mainly because of the invention of open source technologies like Hadoop, and the growing number of open source projects that use them.

Commercial Open Source Hybrids

This brings up an important point, though, because commercial products can use open source components as well. These kinds of commercial products are hybrids, and the fact that you can plug open source components into them says something about the architecture and APIs of the product, which is an important point to consider.

As an example, I like to use the open source ELK stack on my company's appliances, allowing disk space to be shared between packets and events. With ELK, which includes the Elasticsearch, Logstash and Kibana components, an appliance can be used to capture and analyze packets while doing double duty as a SIEM for any security events that are generated as a result of analyzing those packets. Similar set-ups in the APM domain are also very plausible.

The Front End

Now let's turn to the front-end. Ideally, the UI is easy to use. This is where commercial products often come out ahead, while the UI for open source projects might not be as polished.

More importantly, the UI has to perform well and be responsive. Nobody wants to wait 10 minutes for their daily dashboard to populate with charts, or 30 minutes to generate a report on last week's performance. This is a tough one to test as well, because it takes time to collect network data for a week, or a month. So no matter which APM software you are evaluating, test it long enough that you're able to analyze long-term reporting performance before making a choice.
 
These are all tough questions and important considerations to keep in mind when choosing an APM solution. While open source is certainly not free of cost, it is also not necessarily more expensive, and commercial software is not necessarily better. Many other characteristics like cost, support, and performance have to be considered in order to make a well-informed decision.

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Evaluating Commercial vs. Open Source APM

Chris Bloom

Choosing an application performance monitoring (APM) solution can be a daunting task. A quick Google search will show popular products, but there's also a long list of less-well-known open source products available, too. So how do you choose the right solution?

The advantages and disadvantages of open source software versus commercial software are part of an age-old debate. Which is better or worse? Which is more or less buggy? Which is faster or slower? And what about documentation and support, and a million other possible differences? The truth is that they have very little to do with whether the software is open source or not.

Cost and Support

Characteristics like support and cost are of course very important, and can vary greatly between the two. The cost of open source may be something you pay to ramp up on the learning curve, and to tweak the software yourself, or pay somebody else to do it for you.

Support of open source can be very good, if the solution becomes popular within the community. On the other hand, a commercial product may have very polished, organized documentation and well-established support. In other words, aside from the quality of the product itself, a big part of the decision to go with open source or not is your preference on when to invest money in the product and how to get support. With open source, you have a lot of flexibility about when and what you spend your money on.
 
With many open source projects there are the completely free options, usually posted on Github as source. There are also commercial components and support provided for a fee from a company supporting the project. This can make getting started reasonably inexpensive if the open source project's functionality satisfies your requirements. As your requirements change and grow, you can develop new functionality yourself, buy components from others, or just wait until somebody else does the work.

One of the biggest benefits of open source from a cost perspective is that once you have something that works for you, you don't have to keep paying for it annually, like you would for a commercial product. On the other hand, paying for a commercial product gives you a well-defined set of features and characteristics that you can count on, and somebody to call and complain to otherwise.

APM Performance: Speed and Scalability

 
Performance is another good example of a software characteristic that cannot be determined by open source or commercial availability. Performance for APM software is defined by speed and scalability, mostly on the back-end. Sure, the front-end UI must be fast, but the real question and challenge for this type of software is how much analysis it can do on network traffic or flow data.

For smaller networks, let's say 100Mbps or less, this is not much of an issue. But when you start to get above that, the overwhelming volume of packets and flows that must be processed every second exceeds the limits of a single thread. This is where you need to consider whether the solution is multi-threaded or not.

And for networks with speeds in the 10Gbps arena and up, even multi-threaded software on a single server is not going to be enough. In this case, the solution needs to scale by distributing the load across several servers, and aggregating the results into a single pane of glass. In my own experience, I have found open source solutions to be more scalable than commercial products, or at least accessible to more people, mainly because of the invention of open source technologies like Hadoop, and the growing number of open source projects that use them.

Commercial Open Source Hybrids

This brings up an important point, though, because commercial products can use open source components as well. These kinds of commercial products are hybrids, and the fact that you can plug open source components into them says something about the architecture and APIs of the product, which is an important point to consider.

As an example, I like to use the open source ELK stack on my company's appliances, allowing disk space to be shared between packets and events. With ELK, which includes the Elasticsearch, Logstash and Kibana components, an appliance can be used to capture and analyze packets while doing double duty as a SIEM for any security events that are generated as a result of analyzing those packets. Similar set-ups in the APM domain are also very plausible.

The Front End

Now let's turn to the front-end. Ideally, the UI is easy to use. This is where commercial products often come out ahead, while the UI for open source projects might not be as polished.

More importantly, the UI has to perform well and be responsive. Nobody wants to wait 10 minutes for their daily dashboard to populate with charts, or 30 minutes to generate a report on last week's performance. This is a tough one to test as well, because it takes time to collect network data for a week, or a month. So no matter which APM software you are evaluating, test it long enough that you're able to analyze long-term reporting performance before making a choice.
 
These are all tough questions and important considerations to keep in mind when choosing an APM solution. While open source is certainly not free of cost, it is also not necessarily more expensive, and commercial software is not necessarily better. Many other characteristics like cost, support, and performance have to be considered in order to make a well-informed decision.

Hot Topics

The Latest

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 ...

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 ...