We Tried & Compared Top 6 Open Source APM Tools in 2026

TL;DR

The best open source Application Performance Monitoring (APM) tool depends on whether your team needs a unified platform or a focused system for metrics or distributed tracing. SigNoz, Prometheus with Grafana, Zipkin, and Jaeger are strong options because they represent three common approaches: unified observability, metrics-first monitoring, and distributed tracing.

These tools are not interchangeable. SigNoz combines application metrics, logs, distributed tracing, dashboards, and alerts in one OpenTelemetry-native platform. Prometheus and Grafana provide a composable, metrics-first monitoring setup, while Zipkin and Jaeger focus primarily on distributed tracing.

Quick picks:

• SigNoz: Best for teams that want OpenTelemetry-native APM with application metrics, logs, distributed tracing, dashboards, and alerts in one platform.
• Prometheus and Grafana: Best for teams primarily interested in collecting and querying metrics and building monitoring dashboards.
• Zipkin: Best for teams that want a focused system for collecting, searching, and visualizing distributed traces.
• Jaeger: Best for teams building an OpenTelemetry-based tracing deployment with configurable collection pipelines, sampling, and storage.

What counts as an open source APM tool?

Open-source APM and observability tools allow teams to self-host and keep ownership of their telemetry data. However, these tools do not necessarily cover the same parts of application monitoring. Some provide an integrated APM experience, while others handle only metrics or distributed tracing. The following capabilities help determine whether a tool can meet your requirements.

Core capabilities to evaluate for an open source APM tool:

1. OpenTelemetry support: Collect and export telemetry using vendor-neutral OpenTelemetry APIs and SDKs, with OTLP over gRPC or HTTP, so that application instrumentation remains portable across compatible backends.
2. Distributed tracing: Follow requests across services, queues, databases, and external APIs to identify slow or failing operations.
3. Application and infrastructure metrics: Monitor request rates, errors, latency, CPU, memory, and other indicators of application health.
4. Log correlation: Correlate logs with the relevant service, trace, or span to add context during an investigation.
5. Service maps: Visualize dependencies between services and identify where errors or latency originate.
6. Dashboards and alerting: Track important telemetry through custom dashboards and notify teams when defined conditions are met, such as when your API latencies spike or a K8s deployment fails.

You may not need every capability from the same product. If your existing stack already handles metrics and logs, a tracing system such as Zipkin or Jaeger may be sufficient. If you want to investigate metrics, traces, and logs through one interface, a unified APM platform like SigNoz will be a better fit.

How to choose an open source APM tool

The right open source APM tool depends on what is missing from your current monitoring stack, the telemetry volume you expect, and how much infrastructure your team is prepared to operate. Use the following checks to narrow the shortlist.

Verify instrumentation compatibility

Check whether the tool supports OpenTelemetry, and any other tools and protocols you plan to use. Also verify automatic and manual instrumentation for your languages, frameworks, databases, and messaging systems. OpenTelemetry reduces instrumentation lock-in, but queries, dashboards, alerts, and stored data can still require migration work when changing backends.

Calculate the complete operating cost

Open source software may not have a licensing fee, but the infrastructure running it is not free. Estimate the volume and retention requirements for metrics, logs, and traces, along with the effect of high-cardinality attributes.

Count every component the deployment requires, including telemetry databases, metadata stores, queues, object storage, collectors, and load balancers. Then include the engineering time needed for scaling, backups, upgrades, security patches, and incident response.

Test the investigation workflow at your expected scale

A storage engine or architecture does not guarantee good performance for your workload. Run a proof of concept with roughly the same data volume, retention period, and mix of service attributes you expect in production.

Reproduce a slow or failed request and check whether engineers can identify the affected service, inspect its trace, find the relevant logs, and create an alert. Test the queries you expect to run during incidents, not only the default dashboards provided by the tool.

Check production and security requirements

Verify which controls are included in the self-hosted open source edition. Depending on your environment, this may include authentication, RBAC, SSO, audit logs, multi-tenancy, encryption, and telemetry retention controls. If applications may send sensitive information, confirm that your SDK or collector pipeline can remove or mask it before ingestion. Do not assume that every feature shown in the product documentation is included in the open source edition.

Evaluate project health and support

Look beyond GitHub stars. Check recent releases, upgrade documentation, security advisories, issue response times, and whether development depends on a small number of maintainers. For production-critical deployments, determine whether commercial support or experienced service providers are available. This matters when your team cannot afford to diagnose failures in the monitoring system independently.

Remove any tool that fails a required workflow, security requirement, or operational constraint. Among the remaining options, choose the one that provides the shortest investigation path at a cost and maintenance level your team can sustain.

How we evaluated the tools

We assessed each tool using the same areas discussed above: instrumentation compatibility, deployment and operating requirements, application investigation capabilities, production and security controls, and project health and support. We verified product capabilities through official documentation, source repositories, and licensing information.

The table provides a quick summary of each tool’s scope and best fit. The individual sections cover these evaluation areas in more detail.

6 Best Open Source Tools for Application Performance Monitoring

1. SigNoz: Best for unified, OpenTelemetry-native APM

SigNoz is an OpenTelemetry-native APM and observability platform for teams that want one place to investigate application and infrastructure performance. It is available as a self-hosted deployment or a managed service called SigNoz Cloud.

Key features

• Out-of-the-box RED metrics: SigNoz automatically derives Rate, Errors, and Duration metrics from trace data. Instrumented services appear with request rate, error rate, and latency charts without requiring users to build these dashboards manually.
• Distributed tracing: Follow requests across services and inspect individual spans, errors, database calls, and external API calls.
• Cross-signal investigation: Move between application metrics, distributed traces, and related logs when the required trace context is present.
• Dashboards and alerts: Build custom dashboards and create alerts from metrics, logs, traces, and exceptions.
• OpenTelemetry instrumentation: Send telemetry using OpenTelemetry SDKs and collectors through OTLP, reducing vendor lock-in at the instrumentation layer.
• Infrastructure and exception monitoring: Monitor hosts, Kubernetes environments, and application exceptions alongside APM data.

What to validate

• Use the OpenTelemetry Demo to check the path from service-level RED metrics to a trace and its correlated logs.
• Confirm that the available user roles meet your access-control requirements.

Limitations

• A self-hosted deployment makes your team responsible for ClickHouse capacity, retention, backups, upgrades, and the SigNoz services around it.

2. Prometheus and Grafana: Best for metrics-first application monitoring

Prometheus and Grafana provide a composable monitoring setup for teams whose application-performance questions can primarily be answered using metrics. Prometheus collects and queries time-series data, while Grafana provides dashboards, exploration, and alerting.

Key features

• Metrics collection: Prometheus scrapes application and infrastructure metrics over HTTP and supports static configuration and service discovery.
• PromQL: Query, aggregate, and analyze time-series data using the Prometheus Query Language.
• Application monitoring: Track request rates, errors, latency, saturation, and other application metrics exposed by client libraries or exporters.
• Dashboards: Use Grafana to visualize Prometheus data and build dashboards for services and infrastructure.
• Alerting: Define alerting rules in Prometheus and route notifications through Alertmanager, or create Grafana-managed alerts.

What to validate

• Confirm that client libraries or exporters expose the application metrics you need, then check their labels for unnecessary cardinality.
• If an investigation must cross into logs or traces, validate the links and context passed between the separate backends.

Limitations

• Prometheus does not store distributed traces or application logs. Grafana can visualize those signals only after you connect tracing and logging backends.
• Cross-signal investigation requires you to configure and operate those additional backends, data sources, and correlations.
• Long-term storage or deduplicated high availability can introduce further components.

3. Elastic APM: Best for teams already using Elasticsearch and Kibana

Elastic APM extends the Elastic Stack with application performance monitoring. It is a natural option for teams that already use Elasticsearch for operational data and Kibana for dashboards and investigation.

Key features

• Transaction monitoring: Analyze latency, throughput, failed transactions, and the time spent in downstream operations.
• Distributed tracing: Inspect transactions and spans across instrumented services.
• Error monitoring: Capture application errors and connect them with affected transactions.
• Service maps: Visualize instrumented services, databases, messaging systems, and external dependencies.
• Log correlation: Display logs associated with a sampled trace when trace and log correlation is configured.
• Instrumentation options: Collect data using Elastic APM agents or OpenTelemetry. Elastic supports native OTLP ingestion for traces and metrics.

What to validate

• Send data through the OpenTelemetry path you intend to run and confirm that the required fields and APM views are available; OpenTelemetry data does not have full parity across every view.
• Confirm that the security, access-control, and APM features you require are included in the exact Elastic distribution you plan to deploy.

Limitations

• A self-managed deployment requires Elasticsearch, Kibana, and an APM ingestion path such as APM Server or Elastic Agent. Capacity planning, index management, and upgrades apply to the complete stack.
• OpenTelemetry data does not have full feature parity across every Elastic APM view.

4. Uptrace: Best for OpenTelemetry-based, multi-signal monitoring

Uptrace is an OpenTelemetry APM and observability platform for traces, metrics, and logs. It provides application investigation features through service views, trace analysis, dashboards, service graphs, and monitors.

Key features

• Distributed tracing: Search spans and traces, filter them by service or status, and inspect complete request timelines.
• Metrics monitoring: Query application and infrastructure metrics and visualize them through prebuilt or custom dashboards.
• Logs and errors: Search logs and exceptions and connect them with related spans and traces.
• Service graph: Visualize service interactions, dependencies, and request performance.
• Dashboards: Build dashboards from spans, logs, events, and metrics using the UI or YAML.
• Alerting: Create metric and error monitors and send notifications through supported channels.
• OpenTelemetry ingestion: Receive traces, metrics, and logs through OTLP over gRPC or HTTP.

What to validate

• Test backup, upgrade, and recovery procedures across ClickHouse, PostgreSQL, and Redis.
• Confirm that the authentication, SSO, high-availability, and support options in your selected edition meet your requirements.

Limitations

• Self-hosting requires three stateful data systems: ClickHouse for telemetry, PostgreSQL for metadata, and Redis for caching and sessions.
• Backups, upgrades, high availability, and failure recovery must account for all three dependencies rather than a single telemetry database.

5. Zipkin: Best for focused trace collection and lookup

Zipkin is a distributed tracing system for collecting, storing, and searching request traces. It fits teams that want a focused tracing backend without adopting a broader metrics and log platform.

Key features

• Trace collection: Receive spans produced by instrumented applications and persist them through a configurable storage backend.
• Trace search: Find traces by service, operation, tags, duration, and time range.
• Trace visualization: Inspect the spans and timing relationships that make up a request.
• Dependency diagrams: View service relationships derived from collected traces.
• Pluggable storage: Use in-memory storage for testing or persistent backends such as Cassandra, Elasticsearch, or MySQL.
• OpenTelemetry integration: Receive OpenTelemetry traces through Zipkin’s OTLP integration or through an OpenTelemetry Collector configured for Zipkin.

What to validate

• Confirm whether applications will send Zipkin-formatted spans, use Zipkin’s OTLP integration, or route traces through an OpenTelemetry Collector.
• Validate the external access-control layer because the Zipkin UI has no built-in authentication.

Limitations

• Zipkin is not a general-purpose metrics or log backend. Its OpenTelemetry integration accepts traces but does not support metrics; qualifying log events are recorded only as span annotations.
• The Zipkin UI has no built-in authentication. Production deployments must add access control outside Zipkin and operate a suitable persistent storage backend.

6. Jaeger: Best for OpenTelemetry-oriented distributed tracing

Jaeger is a CNCF-graduated distributed tracing platform for monitoring requests across services. Jaeger v2 is based on the OpenTelemetry Collector framework and is designed to operate as a configurable tracing backend.

Key features

• OTLP ingestion: Receive trace data from OpenTelemetry SDKs and collectors over OTLP.
• Trace investigation: Search traces and inspect request timelines, spans, errors, attributes, and service operations.
• Service dependencies: Visualize relationships between traced services through topology graphs.
• Sampling controls: Configure head-based, tail-based, remote, or adaptive sampling.
• Configurable pipelines: Use OpenTelemetry Collector-style receivers, processors, connectors, and exporters.

What to validate

• Verify whether Service Performance Monitoring will use a span-metrics pipeline and PromQL backend or supported trace storage.
• Check how service dependency graphs will be generated with your selected storage architecture.

Limitations

• Jaeger is primarily a trace backend. It does not replace general-purpose metric or log storage.
• Production deployments require persistent trace storage. Service Performance Monitoring may also require a span-metrics pipeline and a PromQL-compatible metrics backend, unless Jaeger calculates the metrics directly from supported Elasticsearch or OpenSearch trace storage.

Which open source APM tool should you choose?

The shortlist becomes easier to navigate when you separate unified platforms from focused monitoring components.

• For unified APM: SigNoz is our recommendation for a new OpenTelemetry-based deployment that needs metrics, traces, logs, dashboards, and alerts. Elastic APM fits teams already operating Elasticsearch and Kibana, while Uptrace fits teams prepared to manage its ClickHouse, PostgreSQL, and Redis architecture.
• For metrics-first monitoring: Choose Prometheus and Grafana when metrics, PromQL, dashboards, and alerting cover your primary requirements.
• For distributed tracing: Choose Zipkin for a focused trace collection and lookup system. Consider Jaeger when you want an OpenTelemetry-oriented tracing backend with configurable collection pipelines, sampling, and storage.

Whichever path you choose, test it using an application and workload that resemble your production environment. The best option is the one that supports your incident workflow without creating more operational work than your team can maintain.

Frequently asked questions

What is the best open source APM tool?

There is no single best option for every monitoring stack. SigNoz is our recommendation for teams that want unified, OpenTelemetry-native APM, while narrower tools may fit teams that only need metrics or distributed tracing.

Is OpenTelemetry an APM tool?

No. OpenTelemetry provides APIs, SDKs, semantic conventions, and collectors for generating and transporting telemetry. It does not provide the storage, querying, visualization, and alerting capabilities of an APM backend.

Are Prometheus and Grafana a complete APM platform?

Prometheus collects and queries time-series metrics, while Grafana provides dashboards and visualization. Together, they support metrics-based application monitoring, but logs and distributed traces require additional backends.

Are Zipkin and Jaeger APM tools?

Zipkin and Jaeger provide distributed tracing, which is an important part of APM. They help investigate requests across services but do not replace general-purpose metrics and log management systems.

Is Elastic APM open source?

Elastic APM spans components and artifacts with different licenses. Elasticsearch and Kibana source outside x-pack is available under AGPLv3, SSPL 1.0, or ELv2, but Elastic’s default distributions remain under ELv2. APM Server source defaults to Apache 2.0 outside x-pack; Elastic provides separate Apache 2.0 OSS binaries, while its default APM Server distribution is under the Elastic License. Verify the exact artifacts in a deployment before describing the complete stack as open source.

Get Started with SigNoz

SigNoz provides application metrics, distributed tracing, logs, dashboards, and alerts in an OpenTelemetry-native platform. You can use SigNoz Cloud or self-host SigNoz in your own environment.

If your applications already produce OpenTelemetry data, configure their OTLP exporters to send it to SigNoz. Otherwise, follow the instrumentation guides for your language or framework. Start with one service and verify that you can move from an application latency or error signal to its trace and related logs.