Top 8 Observability Tools for 2025: Go from Data to Action

Here at groundcover, we're big fans of open source monitoring solutions, log analysis tools, and performance optimization platforms, which are highly flexible and extensible. They’re also (usually) free of cost. All in all, it's hard not to love open source observability software.

We know - that may sound a bit strange, given that not all of the software we build at groundcover is open source (although we do maintain a number of open source repositories). But the fact is that, even though open source isn't always the right way to meet your observability or other needs, it's often a good starting point. 

With that fact in mind, we'd like to walk through the various open source observability solutions on the market today. We'll also discuss how to choose between them – and when it makes sense to opt for a non-open source observability solution.

What is observability, and why does it matter to IT operations?

Before diving into an analysis of open source observability tools, let's discuss what observability means in the first place and why it's so important.

As you probably know, if you're an acolyte of IT buzzwords, observability is the interpretation of the internal state of a complex system based on its external outputs. In other words, when you observe an IT system – as opposed to simply monitoring it – you do more than just collect telemetry data. You also compare and correlate the various types of information available to you in an effort to gain a holistic understanding of what's happening deep inside the system.

The observability concept has been around for decades, but it didn't catch on in the realm of IT until the late 2010s. At that time, many organizations faced the challenge of having to manage a complex, distributed environment – like one that consisted of microservices apps running atop sprawling Kubernetes clusters. This necessitated strategies for understanding the state of those systems that extended beyond conventional monitoring, data analysis, and application performance monitoring. Hence why observability has become a key focus of modern IT operations.

What are observability tools?

Now that we've covered the basics of what observability means, let’s talk about observability platforms and tools.

Observability platforms and tools are – you guessed it – software solutions designed to enable observability. This means they can collect and help teams interpret the telemetry data necessary to infer the internal state of a complex system.

Importantly, when we talk about observability software, we’re not referring simply to any type of monitoring solution. For decades, IT teams have deployed monitoring tools that can do things like track whether servers are up and monitor how much memory or CPU applications are using. Collecting telemetry data like this is part of the observability process, but on its own, it does not constitute observability.

Instead, observability platforms and tools go beyond monitoring by collecting, correlating, and analyzing disparate sets of telemetry data. When you’re dealing with a complex, multi-layered environment like Kubernetes, conventional monitoring isn’t enough because it only gives you a surface-level view of what’s happening. You need observability to dive deeper and truly understand what’s happening under the hood.

Observability tools vs. observability platforms

If you study the observability software landscape, you’ll notice that some developers label their solutions as “tools” while others call them “platforms.” The difference between tools and platforms in this respect is, to an extent, in the eye of the beholder – and the product marketer.

But in general, observability platforms are holistic solutions that provide the full set of capabilities – telemetry data collection, data processing, data analytics, and data visualization – necessary to observe a complex system. A tool would handle only one part of this process; for example, Grafana is a tool that primarily addresses only data visualization, and needs to be integrated with other tools to handle data collection and so on.

Types of observability solutions

Broadly speaking, observability software falls into four main categories.

Features of observability tools

Just because open source observability solutions tend to have narrow areas of focus doesn't mean that they don't share much in common. On the contrary, from a feature perspective, most observability tools provide the same core types of functionality:

• Data collection: They let you ingest data from whichever sources will enable you to observe a system.
• Data analysis: They help you analyze the data you've collected to identify patterns or anomalies that can tip you off to performance problems or risks.
• Root cause analysis: Knowing that a performance problem exists is only half the battle, which is why most observability platforms also provide features designed to help pinpoint the root cause of an issue.
• Data management: Some observability solutions offer features to help manage the data they ingest and store. For example, they might assist with log rotation or the archiving of data after you've completed an analysis of it. 

Where the various types of observability solutions differ is in the use cases they support. They also tend to vary with regard to the types of data they can collect and analyze, but that's because different data sources align with different use cases.

Benefits of observability software

Although the exact features of observability platforms and tools vary, they all deliver the same core benefits, such as:

• Enhanced problem detection: By providing deep insight into what’s happening inside a complex system, observability solutions make it easier for teams to identify issues. For example, they could help you determine that a Kubernetes node’s CPU utilization is steadily increasing while at the same time, a pod on the node is frequently restarting – a sign that there is probably buggy code in the pod that is causing to crash and restart, all the while sucking up high levels of CPU. With observability solutions, you can get ahead of this issue before the node fails completely.
• Faster remediation: The ability to correlate disparate types of data using observability software helps teams get to the root cause of issues and fix them faster. Again, in the example we just mentioned, being able to correlate a resource utilization anomaly on a node with unusual behavior by a pod would help troubleshoot the issue fast, increasing the chances of resolving it before an outage occurs.
• Stronger team collaboration: At many organizations, the responsibility for collecting and interpreting various types of data tends to be siloed. One team might track the health of servers and other infrastructure, for example, while another manages application performance. Because observability solutions provide insights into how all relevant hardware and software resources are performing, they are valuable as a means of driving collaboration between teams. Put another way, observability software breaks performance data out of its silos so that all stakeholders can work with it efficiently.
• Better security insights: While performance monitoring and management is typically the main focus of observability, some tools in this category also support security incident detection and remediation. In a world where businesses continue to struggle with cyberattacks, the ability to leverage all available data for security purposes is critical.

Put simply, observability tools offer the key benefit of helping organizations achieve maximum value from the data that their applications and infrastructure generate. 

8 top open source observability solutions

IT tool developers have responded to the observability craze by building a number of solutions, including several fantastic open source observability solutions. Here's a look at what we consider to be the top four contenders in the open source observability market.

As you'll see, although these tools overlap a bit functionality-wise in some cases, each solution aligns with a different type of observability need – so rather than thinking of these as either-or open source observability solutions, think of them as a set of tools that, when combined together, can form the foundation for a modern observability strategy.

1. ELK stack (or OpenSearch) for log analysis

The so-called ELK stack consists of three components:

• Elasticsearch, a distributed analytics engine that can run queries for a variety of use cases, including log analysis.
• Logstash, a data processing pipeline that can support virtually any type of data source.‍
• Kibana, which provides data visualization to help interpret complex sets of information.

By combining these tools together, you get a more or less open source tooling stack that lets you ingest log data, search it, and visualize it using a unified set of tools. We say "more or less" because Elasticsearch and Kibana aren't officially open source at present; since 2021 they have been "open code" per Elastic, the company that maintains them. We won't get into the politics surrounding that choice of label or the differences between open source and open code, but you can read more about the status of the ELK stack and the debates it spurred within the open source community if you'd like (Logstash is unequivocally open source, for the record).

We'll also note that if you're not comfortable with the open code status of parts of the ELK stack, you might be interested in OpenSearch, which is a fully open source solution derived from the ELK stack. Again, there are some politics and history here that we won't get into, but suffice it to say that OpenSearch was basically created to give users a 100 percent open source version of ELK.

Allow us to note, too, that there are variants on the ELK stack. For example, you can swap out Logstash for an alternative open source log collector, like Fluentd, in which case you'd have an EFK stack instead of an ELK stack.

Discussing the pros and cons of different log collectors is beyond the scope of this article, but we note the flexibility here because the ability to customize your ELK stack based on your tooling preferences is part of what makes ELK (or whatever acronym aligns with your tool choices) so powerful.

2. Prometheus for metrics and performance optimization

The ELK stack is a great way to construct an open source solution for log analysis, but what about working with metrics that aren't stored in logs?

That's where Prometheus and Grafana come in. Prometheus is an open source monitoring tool that lets you collect time-series metrics from a variety of different applications and environments – including modern, cloud-native apps. You can pair Prometheus with Grafana (more on that in a moment) to visualize data and identify important insights.

An important limitation to note is that Prometheus doesn’t directly support tracing. It’s possible to import tracing data into the tool, but you need to convert it first to a format that Prometheus can interpret, or use another tool (like Grafana) alongside Prometheus to visualize data.

3. Grafana for open source data visualizations

Speaking of visualizations, Grafana is the go-to solution when you want an open source option for visualizing observability data. Grafana can create a wide variety of charts and graphs – such as time-series displays, heatmaps, and histograms, to name just a few popular choices – that represent metrics, logs, and traces in visual form.

This is important because most other open source observability solutions don’t offer features for visualizing data; they just help you collect and manage the telemetry data itself. But if you want to make sense of data at scale, you’ll want an open visualization solution like Grafana.

4. OpenTelemetry and Jaeger stack for distributing tracing

Metrics, logs, and traces are the three so-called pillars of observability – but most open source observability solutions only focus on the latter two types of data. You need a separate solution for distributed tracing, which lets you track the movement of data within distributed applications to pinpoint the source of errors.

If you want to run distributed traces using open source observability solutions, the go-to solutions are OpenTelemetry and Jaeger. OpenTelemetry is a set of APIs and SDKs that allow you to expose observability data from within an application in a standardized, efficient way, and Jaeger is designed to help monitor and analyze interdependent components within an application.

So, when you pair OpenTelemetry with Jaeger, you get a complete toolchain for collecting and analyzing traces within your cloud-native microservices apps.

Lest we leave readers with the impression that distributed tracing is the only thing OpenTelemetry is good for, we should note that it's not. You can use OpenTelemetry to collect virtually any type of observability data, not just traces, and connect it to a variety of tools, not just Jaeger. But distributed tracing is the use case you'd target if you chose to deploy OpenTelemetry and Jaeger together.

5. Zipkin, another open source tracing option

Alongside Jaeger, Zipkin is another powerful open source tracing tool. In general, Zipkin is easier to use than Jaeger, making it an attractive solution for teams that want to get started with tracing quickly. In addition, while Zipkin and Jaeger both support a variety of programming languages and frameworks, Zipkin also offers particularly good support for Java – so if you need to observe Java apps, Zipkin is probably the best option for you.

6. OpenLens for Kubernetes infrastructure monitoring

It's possible to leverage some of the observability solutions we've already discussed, such as the ELK stack and Prometheus, to monitor the infrastructure that powers your Kubernetes clusters.

However, Kubernetes is its own special beast from an infrastructure perspective. It relies on a unique set of infrastructure components and concepts – control plane nodes, worker nodes, an API server, an etcd store, and so on – and observing them with tools not designed specifically for Kubernetes doesn't always go as smoothly as one desires.

OpenLens was built with this need in mind. It lets you monitor the health of the various components of your Kubernetes infrastructure to ensure that your workloads have the resources they need to perform at their best.

Unfortunately, as of early 2025, the future of the OpenLens project seems uncertain, and active development seems to have ceased. However, you can still find and install builds of the tool.

7. K9s, a CLI observability solution for Kubernetes

If you like the idea of an open source observability solution designed specifically for Kubernetes, but you’re worried about the long-term outlook for OpenLens, you might like K9s.

Like OpenLens, K9s is built especially for managing Kubernetes environments. Unlike OpenLens and most other open source observability solutions, however, K9s is a command line-only tool – which is great if you’re a Kubernetes admin who likes being able to do everything from the CLI.

K9s isn’t designed for observability alone; it offers a broad range of capabilities for managing Kubernetes clusters and workloads. However, it provides observability features like the ability to view cluster metrics and drill down into errors.

K9s isn’t an ideal solution if you want telemetry visualizations or a solution that integrates easily with other observability tools. But if you want a simple, CLI-based management and observability option for Kubernetes, it merits close consideration.

8. Graylog for open source security observability

While most observability platforms and tools focus on providing the insights necessary to optimize application health and performance, some are geared toward identifying security risks. Graylog is a prime example.

Graylog is an open source solution for collecting, storing, and analyzing logs. Its main capabilities include detecting anomalies and security events. Effectively, it’s what’s known as a Security Information Incident and Event Management (SIEM) tool, but we mention it because it uses the principles of observability to manage security risks (note, however, that Graylog focuses on leveraging insights from logs alone; it doesn’t leverage metrics or traces extensively).

What to look for in observability software

As you weigh the various open source observability solutions available today, you’ll want to consider factors like the following to identify the best option for your needs:

• Ease of use: Observability is inherently complex, and some tools can be challenging to configure and use. This is especially true in cases where tools require custom instrumentation (instrumentation means exposing data for collection) to make data accessible, or if you have to set up and manage integrations between tools manually. Tools that are preconfigured out-of-the-box with capabilities that support common observability use cases typically require less effort to deploy effectively.
• Supported integrations: As we’ve mentioned, you often need to combine observability solutions together to implement all desired functionality. For example, you might use one tool for data collection and another for data visualization. It’s important to assess which integrations each tool supports so that you can be sure you’ll be able to build a toolset that addresses all of your needs.
• Scalability: Open source observability solutions can generally work at any level of scale – meaning they are effective no matter how many (or how few) resources or data sets you have to work with. However, some tools may not work as efficiently when deployed on a large scale. This is especially true of tools that can be hosted on just one server at a time, as opposed to being deployed across a cluster of servers to increase scalability and availability. If you can only run one instance of your tool using one server, that server’s capacity will impose a ceiling on the tool’s ability to perform at scale.
• Cost-effectiveness: While most open source observability offerings are free of licensing costs, you usually need to pay for infrastructure to host them and the data they process or store. In addition, some open source tools are available via a “freemium” model in which the core tool is free but you need to pay for extra features you may want to use. Given this, it’s important to consider what the total cost of the tool will be and whether you’re getting full value for what you’re paying. A tool that uses high levels of CPU and memory, for example, might cost you more than it’s worth because you have to pay for the infrastructure that supplies that CPU and memory, even if the tool itself is free.
• Alerting, notifications, and reporting: Some observability tools offer built-in features for alerting you about potential issues. Others leave it to you (possibly with the help of external notification and alert management software) to identify and report issues. If you want these capabilities built into your tools, choose a solution accordingly.
• Security capabilities: As we’ve noted, most observability solutions are designed to address performance-related use cases, but some offer security features as well. Most open source observability solutions only do one or the other, so if you want both application performance observability and security observability, you’ll typically need to deploy multiple solutions.
• Licensing terms: Different open source observability tools are governed by varying open source licenses, and the licensing terms can vary widely. For example, some licenses allow you to modify source code without having to share your modifications publicly, while others would require you to share your source under most circumstances. For these reasons, it’s worth paying close attention to the licensing terms of the tools you’re considering and ensuring they align with your intended use cases.

Challenges of open source observability tools

There are many good reasons to choose open source observability solutions – especially the fact that they’re usually low in cost and highly flexible. However, open source observability platforms and tools can also present some challenges across key categories that impact how businesses use the tools.

What this means is that you should choose open source observability tools if you have the in-house staff expertise and availability to handle the rough edges of open source solutions. Otherwise, opting for open source may mean trading one set of downsides (like paying for software) for another (like going insane when your software doesn't work and the only people available to help are randos on Stack Exchange who may or may not know what they're talking about).

By the way, a desire to mitigate these challenges while still providing access to powerful open source observability is a key part of the vision behind groundcover. We built groundcover in part using open source observability technologies like eBPF and Grafana. But we did the hard work of integrating them into a user-friendly, scalable, cost-effective observability platform that comes with professional support – so users get the benefits of open source observability without the hassle or risk.

Making the most of observability platforms and tools, no matter where they come from

If you've decided that open source observability tools are the right fit for your needs – either because you weighed the advice we gave you above, or because you treat everything Linus Torvalds says as an absolute truth that no one should ever question – you now know which tools to look for.

Alternatively, if you want an observability solution that is less of a hassle, scales with your needs, and was built for modern apps, check out groundcover.

FAQ

Is open source observability software free?

In most cases, open source observability solutions are free to use – although some are distributed under "freemium" models in which users pay fees to access additional features that are not available through the open source version of the tool. Keep in mind, however, that even when tools are free to use, users must typically pay for the infrastructure necessary to host the tools and store observability data.

What are the main differences between open source and closed-source observability solutions?

One difference is licensing and pricing. Most closed-source observability platforms and tools cost money to use. That said, another key difference is that closed-source tools usually come with professional support. They also tend to be easier to use.

Where can I find open source observability software?

A variety of open source observability solutions are available today – such as Grafana, Jaeger, and Zipkin, to name three popular options. In addition, some commercial observability platforms, including groundcover, leverage open source observability technology "under the hood," making it possible for users to take advantage of flexible, open source solutions without having to configure and manage them on their own.