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OBSERVABILITY VS MONITORING

Open Source APM

Five open-source APM tools reviewed side by side: features, architecture, deployment complexity, and community support for cloud application monitoring.

5–8 minutes
Denton Chikura

CHAPTERS

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    The quick download:

    Open-source APM tools provide distributed tracing and monitoring without licensing costs, but each one suits different environments and skill sets.

    • Jaeger and Zipkin are lightweight distributed tracing tools that deploy quickly via Docker and integrate well with Kubernetes and microservices architectures.

    • Prometheus, paired with Grafana, handles time-series monitoring and visualization on Kubernetes, with native CNCF integration and flexible alerting.

    • Elastic APM offers deep integration with the ELK stack for teams already invested in Elasticsearch, but its multi-component setup adds complexity.

    • Evaluate each tool against your deployment environment, team expertise, and storage requirements before committing to a production rollout.

    Open Source APM 

    This guide covers five popular open-source Application Performance Monitoring (APM) tools that DevOps teams and network administrators use to manage cloud applications in production. Each review examines features, functionality, and community adoption.

    Application monitoring tools

    For every website and mobile application, system logs are the central source of data on how the software operates on network hardware during production. Many automatically generated system logs from web servers are rough, unparsed, and hard to read. Application Performance Monitoring (APM) tools were developed by IT companies as a better way to display this data in historical charts for network analysis.

    Application Performance Monitoring (APM) is a process for discovering, tracing, and diagnosing the behavior of cloud software applications in production. APM tools strengthen monitoring by providing deeper visibility into network topologies and improved metrics on the user experience for developers. One of the main advantages of APM tools is their ability to diagnose the root cause of common issues, such as slow UX loads or code errors.

    Application Performance Monitoring overview

    As APM is a vast topic with many areas of specialization, the common characteristics are presented in the following table:

    Application Performance Monitoring life cycle.

    Open source APM tools

    The APM market is well-developed, with many DevOps tools available from public cloud service providers, established IT vendors, and innovative startups. This list of five open-source APM tools examines projects commonly used in production for cloud application support, based on an analysis of their features and advantages. In this review, we selected:

    1. Jaeger
    2. Zipkin
    3. Prometheus (with Grafana as user interface)
    4. Elastic APM
    5. Pinpoint

    The sections below compare each tool across main features, system architecture, deployment complexity, recommended usage patterns, and UI interface. The ratings weigh ease of deployment, licensing cost, and production readiness.

    Jaeger

    Jaeger is an open-source, end-to-end distributed tracing tool, created by Uber in 2017. Jaeger is a CNCF-incubated project that monitors transactions in complex distributed systems. It’s a strong addition to any cloud runtime system based on a microservices architecture and is now part of Red Hat’s OpenShift Service Mesh, which integrates with both Kubernetes and OpenStack.

    Teams use Jaeger in production to debug cloud software errors, monitor transactions, and optimize hardware performance.

    Architecture

    • Backend: Go
    • UI: React/Javascript
    • Storage: Cassandra 3.4+, Elasticsearch (5.x, 6.x, 7.x), Kafka
    • Components: agent, collector, query, and an ingestor
    Jaeger “direct to storage” architecture

    Main features

    • Distributed transactions monitoring – the ability to track down each transaction in the entire application stack (from the application to the database server).
    • Performance and latency optimization – the ability to monitor system behavior during runtimes and catch bottlenecks.
    • Root cause analysis – provides a deep dive into queries and systems that can help point out the issue.
    • Service dependency analysis – the ability to visualize how system components are interconnected and how they communicate.
    • Distributed context propagation – passing the metadata from various steps during the request executions to connect data from multiple checkpoints within the system.

    Deployment

    The main Jaeger components are the agent, collector, query, and ingestor. Each component has a specific role to play in Jaeger’s backend. The four main components of Jaeger are available as open-source code released as Docker images. For Kubernetes and OpenShift deployments, orchestrations are available on GitHub.

    With these images and cloud orchestration, Jaeger’s deployment is relatively straightforward for experienced network administrators and data center engineers.

    Jaeger can easily be deployed from your localhost by running this simple Docker command:

    Once deployed, Jaeger can be accessed via a browser on https://:1668

    User Interface

    Jaeger has a simple UI that helps administrators track problematic transactions and profile them visually through hierarchies and charts.

    Jaeger UI, transactions profiling

    Jaeger is relatively simple to integrate and lightweight in terms of requirements for processing on hardware. It’s recommended to include Jaeger in:

    • Kubernetes Deployments
    • Istio Deployments
    • Microservices Architecture

    Jaeger adds another dimension to your cloud service mesh and enables easier tracing of code errors while operating at an abstraction level that doesn’t interfere with running code or database queries.

    Zipkin

    Zipkin is a distributed tracing system for troubleshooting latency in service architectures. It was launched by Twitter in 2012. Zipkin includes an instrumentation library that allows developers to build reports via HTTP, Kafka, Apache ActiveMQ, gRPC, or RabbitMQ. There are also options for the persistent storage of metrics data for historical search and chart generation with Apache Cassandra or Elasticsearch.

    Architecture

    • Language: Java
    • Storage: Cassandra, Elasticsearch, and MySQL
    • 4 components: collector, storage, search, web UI

    An instrumentation library needs to be created to send traces to Zipkin. There are different Zipkin libraries available for the most common cloud runtime environments. Teams typically send network event data via Kafka or via an HTTP API. The requirements for historical data storage will depend on the project.

    Zipkin architecture diagram

    Main features

    • Collection and lookup of data – monitoring and visualizing application behavior.
    • Dependency diagram – the ability to show how many requests went through each system layer.

    Deployment

    The easiest way to deploy Zipkin is by using a self-contained Zipkin server executable jar file:

    Another option is to use a Docker image and run it by using the command:

    Once deployed Zipkin will be available on https://:9411/zipkin/.

    Jaeger/Zipkin comparison

    Jaeger is built on top of Zipkin and adds a few important features to the base functionality. Jaeger is more aligned with the current CNCF ecosystem and has seen more recent releases and tooling updates. On the other hand, Zipkin is lightweight. It has multiple libraries for different platforms and can be used to build on top of them. Both solutions focus on distributed tracing and will yield similar results. Which one to choose depends on the particular details of your cloud software deployments.

    Language support:Community:Container type of deployment:Non-containerized deployment:
    JaegerMore official languages supportedMore flexible, scalable, and performant
    ZipkinHas a larger developer community; More GitHub contributors, stars, and supportersMore mature and simple without heavy add-on requirements

    User interface

    Similar to Jaeger, Zipkin provides a simple UI interface for tracking down transactions. It also enables an advanced search based on trace IDs if applicable.

    Zipkin UI, transaction profiling, and search

    Prometheus

    Prometheus was created in 2012 by SoundCloud and is CNCF’s second hosted project after Kubernetes, built for cloud system monitoring and alert automation. Prometheus collects and stores data as time series, enabling a wide variety of analytics add-on features.

    Architecture

    • Language: Go
    • Storage: In memory and on the local disk in a custom format
    • Visualization: Grafana or any other API consumer
    • Components: Prometheus storage, client libraries, push gateway, exporters, alert manager
    Prometheus architecture diagram

    Main Features

    • Dimensional data model – Ability to present the time series by metric name and key/pair values.
    • Powerful queries – PromQL as a query language that helps to add dimensionality into the system.
    • Great visualizations – Variety of graphics and dashboards to provide better data analytics displays.

    Deployment

    Prometheus can be deployed:

    • Using pre-combined libraries available on the Prometheus downloads page
    • From source by installing Go, Node.js, npm, and using the makefile available on the official GitHub account
    • Using Ansible, Chef, Puppet, SaltStack, etc. as a configuration management solution from third-party contributors.
    • Using Docker by building from images available on DockerHub via simple command:
    docker run --name prometheus -d -p 127.0.0.1:9090:9090 prom/prometheus

    After installation, Prometheus is available on https://:9090/.

    Grafana visualization

    Since the 2015 release of Grafana 2.5.0, Prometheus has featured Grafana’s built-in support for creating data visualizations. To integrate Grafana data charts and graphs using Prometheus, install Grafana following the official instructions.

    Once installed, Grafana is available on: https://:3000/.

    The next step is to add Prometheus as a data source and create a Prometheus graph. More details can be found on the Prometheus and Grafana documentation.

    See how LogicMonitor unifies APM, infrastructure monitoring, and digital experience in a single platform.

    Open-source APM tools give you strong building blocks for tracing and metrics. LogicMonitor unifies those signals with infrastructure and digital experience monitoring to surface root causes faster.

    FAQs

    What’s the difference between distributed tracing and APM?

    Distributed tracing is a subset of APM focused specifically on tracking requests as they flow through multiple services. APM is broader and includes metrics collection, log analysis, alerting, and performance visualization in addition to tracing. Tools like Jaeger and Zipkin specialize in distributed tracing, while Elastic APM and Prometheus cover a wider APM scope.

    Which open-source APM tool works best with Kubernetes?

    Prometheus integrates natively with Kubernetes as CNCF’s second hosted project, and Grafana provides the visualization layer. Together they handle data collection, dashboards, and alerting. Jaeger also integrates well with Kubernetes for distributed tracing through Istio and OpenShift Service Mesh.

    Can open-source APM tools handle enterprise-scale environments?

    Open-source APM tools can handle significant scale, but enterprise environments often require additional investment in storage backends, high availability configurations, and operational expertise. Tools like Jaeger support Cassandra and Elasticsearch for scalable storage, and Prometheus offers federation for multi-cluster setups. Teams should evaluate whether the operational overhead of managing these tools at scale fits their capacity and requirements.

    How does LogicMonitor support APM and distributed tracing?

    LogicMonitor’s LM Envision platform includes built-in APM capabilities covering infrastructure, cloud, containers, logs, and application traces across hybrid environments. Open-source tracing and metrics tools provide strong building blocks; LogicMonitor extends that foundation by unifying those signals with Internet performance monitoring, real user experience data, and infrastructure metrics in a single platform. Edwin AI correlates across traces, metrics, logs, and digital experience data to reduce alert noise and surface root causes earlier, so teams can respond before issues escalate.

    How do I choose between Jaeger and Zipkin for distributed tracing?

    Both tools provide similar distributed tracing functionality. Jaeger supports more programming languages officially and offers more flexible, scalable container deployments. Zipkin has a larger developer community and is more mature with simpler, non-containerized deployment options. The choice depends on your existing technology stack, container strategy, and community support preferences.

    By Denton Chikura

    Technical Writer

    Denton Chikura is a technical writer and longtime observability advocate focused on helping site reliability engineers and engineering teams discover the tools and capabilities that strengthen internet resilience. He works at the intersection of monitoring, performance, and infrastructure to make complex systems more understandable and usable, bridging the gap between deep technical detail and real‑world operations. His goal is to help teams build faster, detect issues earlier, and recover smarter, ultimately making the internet a better, more reliable place for everyone.

    Disclaimer: The views expressed on this blog are those of the author and do not necessarily reflect the views of LogicMonitor or its affiliates.

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