How to Choose the Right Application Monitoring Tool

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Single-pane-of-glass solutions and observability pipeline solutions each have pros and cons. The one that's best for you depends largely on your organization's needs.

Application performance monitoring (APM) is a key focus for IT teams today—and for good reason.

Tracking metrics such as response time, transactions per second and requests per second are an important user experience indicator. But APM has a major limitation: It tells you if your system is not working, not why it isn’t. Without analyzing your logs and metrics for deeper insights, your business won’t have the necessary information to optimize your level of service.

The solution? Observability.

Observability tools can be divided into two distinct categories:

  1. Single-pane-of-glass solutions, which prioritize simplicity and convenience.
  2. Observability pipeline solutions, which maximize the amount of data you can aggregate and analyze.

Which method of application performance monitoring is better for your business? Unfortunately, there’s no one answer. Indeed, both the above-mentioned categories have essential roles to play in modern observability strategies—and you may need to deploy them simultaneously to meet your needs.

Let’s break down the functionalities of single-pane-of-glass tools and observability pipeline tools, respectively, and help you decide which one is best for you:


1. Single-Pane-of-Glass Tools

A single-pane-of-glass observability tool provides a central console where you can track data analytics and alerts. The data itself typically comes from multiple sources—such as logs and metrics from various parts of your infrastructure and applications— but the observability tool consolidates it around a single interface and set of dashboards.

Most single-pane-of-glass solutions aim to provide some ability to “drill down” into particular data sets to gain more context on a pattern or anomaly or explore how different data sets relate to each other. (Also read: How To Train Your Anomaly Detection System To Learn Normal Behavior in Time Series Data.)

However, because all data needs to fit within a central interface, the level of analytical nuance and depth single-pane-of-glass tools support is nuanced.

That is also the case with the volume of data these tools can collect and the variety of data formats and types they support. A single-pane-of-glass solution typically can’t capture and analyze every data stream from every resource in your IT estate. Instead, it focuses on the most important and accessible ones: production applications and infrastructure as well as systems that expose metrics and logs in an easy-to-collect way.

The tradeoff for limited analytics depth and data coverage is that single-pane-of-glass tools are easier to use: There’s only one interface to learn and a small volume of data collectors to configure.

2. Observability Pipeline Tools

An observability pipeline tool collects and analyzes observability data in your IT estate. From there, the goal is to enrich it and direct it wherever makes the most sense for analysis.

Instead of requiring all data to be analyzed in a single console, using whichever methods that console supports, observability pipeline tools provide the flexibility to analyze data in many different ways. Different teams within an organization can interpret the same data sets in different ways—which may be helpful if, for instance, your IT team wants to observe resources for performance management purposes while your security team wants to monitor the same resources to detect security risks. (Also read: The 7 Basic Principles of IT Security.)

Just as important, observability pipeline tools let you work with data in various formats. Whether you’re dealing with conventional data from applications or infrastructure, or data sources like data streams from IoT devices, observability pipelines support them all.

Observability pipeline tools may provide some built-in analytics features. But they are flexible enough to work with whichever third-party analytics tools your teams prefer to use.

Although observability pipeline tools are more flexible and scalable, they’re no more difficult to configure than single-pane-of-glass tools. After establishing the data connectors, you can work with any data in any volume.

You will need to set up multiple data destinations. Still, the tradeoff for that effort, as noted above, is that you gain the flexibility of analyzing your data in whichever ways you need—instead of being limited to one console and one set of features. (Also read: Destroying Silos With Integrated Data Analytics Platforms.)

How to Choose

Both types of observability have their place within modern observability strategies. Depending on your organization’s observability requirements, a particular tool may work better than another—or you may need both.

Single-pane-of-glass observability solutions may be best for your business if:

  • Your IT environment is not complex and consists of conventional resources (like applications and basic infrastructure). (Also read: Redefining IT Decision-Making in the Age of SaaS.)
  • Keeping things simple is more important to you than optimizing observability.
  • Your teams plan to investigate alerts manually and rely on observability tools primarily to generate those alerts.

Observability pipelines may be best if:

  • You have a large volume of data, and a large number of data sources, to analyze (or you expect your environment to grow in scale over time).
  • Different teams within your organization have different observability needs and tool preferences.
  • You want to avoid becoming dependent on a specific data analytics tool or data storage platform.
  • You have a large volume of machine data, for which observability pipelines are especially well-positioned.

Using both types of solutions together makes sense if you want to transition from conventional monitoring to observability. In this case, you likely deployed a single-pane-of-glass tool for monitoring purposes before your observability needs grew complex. If that sounds like your business, continue using your single-pane-of-glass solution alongside a newer observability pipeline. This approach gives more flexibility and has more granularity.

You may also choose to deploy a single-pane-of-glass tool for monitoring specific systems while relying on an observability pipeline to deliver comprehensive visibility into all of your IT resources. The resources your observability pipeline looks into can include those your single-pane-of-glass tool can’t support well. Of course, the drawback here is that you’ll need to maintain two separate sets of observability tools, so this isn’t an ideal long-term strategy. If you’re using both solutions together as your observability needs develop, it’s a good idea to transition to an observability pipeline-based approach when you can.


Observability tools can help you better understand the “why” behind your application performance monitoring data. While single-pane-of-glass solutions and observability pipeline solutions each have pros and cons, the one that’s best for your business depends largely on your needs.

But once you find the tool that works for you, you can determine the necessary actions to engineer an optimal user experience.


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Peter Cho

Peter Cho is the vice president of product management at LogDNA. Previously, Peter served as VP of Product Management at, where he led product development on a cloud-native developer tools marketplace, building out a series of APIs, integrations, and dashboards while simultaneously scaling up the partner ecosystem from scratch. As the Head of Heroku’s Ecosystem Program (a Salesforce company), Peter owned and scaled a partner ecosystem of over 150 third-party vendors and developed partnerships with Facebook (via Parse) and Slack to increase mutual developer adoption. He has also advised a number of developer-focused companies including Ionic, SendGrid and Twilio,…