The right open-source application performance monitoring (APM) choice depends on what you need to observe and what you already operate. Elastic APM is an integrated APM system; Jaeger, Grafana Tempo and Zipkin focus on distributed tracing; OpenTelemetry and its Collector provide instrumentation and telemetry pipelines, not an APM backend. SigNoz is an OTLP-native unified observability platform. The other candidates—Apache SkyWalking, Pinpoint, OpenObserve and Uptrace—are worth evaluating against your languages, deployment model and required features.
There is no evidence here for a universal winner by cost or speed. The useful comparison is architectural: which parts of APM you need, how data gets from your applications to storage, and how much of that system your team is prepared to run.
What counts as an open-source APM tool?
“APM” is often used for several distinct layers: libraries or agents that instrument applications, pipelines that receive and route telemetry, and backends that store, query and display it. Some projects cover multiple layers; others deliberately do one job. Comparing them as if all ten were interchangeable products leads to poor choices.
OpenTelemetry is the clearest example of the distinction. Its documentation says, “OpenTelemetry is not an observability backend itself.” It is a vendor-neutral framework and toolkit for generating, collecting and exporting telemetry. You still need a destination that stores and exposes the data. An OpenTelemetry Collector is a pipeline component—not a user-facing APM system—and normally forwards telemetry to a backend such as Jaeger, Tempo, Elastic APM or SigNoz.
Recommended Free Tools
The options below therefore include complete APM or observability platforms, tracing backends and infrastructure used to assemble a stack. Project scope, packaging, licenses, supported runtimes and hosted offerings can change; check the current project documentation before choosing a deployment.
Compare the 10 options
| Tool | What it is best understood as | Good starting point when | What to verify |
|---|---|---|---|
| Elastic APM | An APM system on the Elastic Stack. | Your team already uses Elasticsearch and Kibana, or wants APM and logs in a shared search and analytics platform. Elastic describes collecting incoming-request response times, database queries, cache calls, external HTTP calls, unhandled errors and metrics. | Whether the Elastic Stack and its operational requirements fit your team; which instrumentation path suits your applications; and how you will configure retention and queries. Elastic documents a self-hosted APM Server path as well as current OpenTelemetry collection guidance. |
| Jaeger | An open-source distributed-tracing backend; the OpenTelemetry ecosystem registry lists it as OSS with native OTLP support. | You primarily need distributed traces and want to evaluate a long-standing tracing option. | Storage backend, retention, query performance under your workload, sampling, and whether traces alone provide enough context for your operators. |
| Apache SkyWalking | An open-source APM and observability project; the OpenTelemetry registry lists it as OSS with native OTLP support. | You want to assess application monitoring and service topology alongside trace search. | Agent and language coverage for your actual estate, current release and runtime support, storage and deployment needs. |
| SigNoz | An open-source, OTLP-native observability platform listed in the OpenTelemetry registry. | You want a unified interface for traces, metrics and logs, rather than stitching together independent backends. | Its current self-hosted packaging, data-retention controls, supported runtimes and how its query and ingestion model match your workload. |
| Grafana Tempo | An open-source distributed-tracing backend designed for the Grafana ecosystem. | You already use Grafana or are willing to assemble a broader Grafana observability stack around traces. | How you will collect and forward traces, search them, and connect them to logs and metrics. Grafana documents trace search, metrics generated from spans, and links between traces, logs and metrics. |
| OpenTelemetry Collector | A vendor-neutral telemetry pipeline for receiving, processing and exporting data—not an APM UI or storage backend. | You want portable instrumentation and control over where telemetry is routed. | Which receivers, processors and exporters your planned pipeline needs, and which backend will store and display the data. |
| Zipkin | A focused open-source distributed-tracing backend that can be paired with OpenTelemetry instrumentation. | You want to evaluate a tracing component rather than deploy a unified logs-and-metrics APM suite. | Storage, sampling and UI requirements compared with Jaeger and Tempo, as well as language coverage for your applications. |
| Pinpoint | An open-source application performance and distributed-tracing option, particularly relevant to JVM-oriented monitoring. | Your applications make JVM monitoring a central evaluation criterion. | Current agent, runtime and release support for every language and framework you need. Do not assume a JVM emphasis covers a mixed-language estate. |
| OpenObserve | An open-source observability backend candidate for logs, metrics and traces. | You are looking for a single platform and want to compare its operating model with other unified options. | Ingestion and query model, retention controls, OpenTelemetry compatibility and deployment requirements. |
| Uptrace | An OpenTelemetry-oriented observability and APM backend candidate. | You want to evaluate a backend built around OpenTelemetry data. | Current self-hosted packaging, supported runtimes, storage requirements and UI workflow versus SigNoz, Elastic APM and Grafana components. |
The OpenTelemetry ecosystem registry identifies Jaeger, Apache SkyWalking and SigNoz as open-source options with native OTLP support, and lists open-source observability products from Elastic and Grafana Labs as well. Registry inclusion is useful evidence of ecosystem fit, not a substitute for checking a project’s current license, release activity or exact feature support.
How to choose based on your monitoring needs
If you need traces, metrics, logs and errors together
Start by deciding whether “unified” means one product interface or simply one place where teams can correlate data. Elastic APM is a natural candidate for teams already operating Elasticsearch and Kibana, while SigNoz is a candidate for teams looking for an OTLP-native interface across traces, metrics and logs. OpenObserve also belongs on a unified-platform shortlist. Compare their retention, query workflow, storage and runtime coverage using your own telemetry and operational requirements; the available descriptions do not establish a fair speed or price ranking.
If distributed tracing is the immediate problem
Evaluate Jaeger, Tempo and Zipkin as tracing backends. Tempo is especially compelling when Grafana is already part of your environment and you want trace search connected with logs and metrics. Jaeger and Zipkin are also tracing components, so plan separately for the other signals and operational workflows you need. Apache SkyWalking may be relevant if service topology and broader application monitoring are important, but validate its agent and language coverage first.
If portability and routing matter more than a bundled UI
Instrument with OpenTelemetry and use the Collector as a pipeline, then select a backend independently. This separates application instrumentation from the storage and visualization choice. It can reduce dependence on one backend, but it also means the team must choose, operate and connect the other components. A Collector by itself will not give you a trace-search interface, dashboards or a data store.
If your application estate is specialized
For a JVM-heavy estate, include Pinpoint in an evaluation, but confirm support for the precise runtimes and releases you run. For any tool, test the frameworks and deployment patterns that matter in practice—not just the headline language list. A tool’s general category does not establish compatibility with your specific application versions.
A practical architecture for a portable APM stack
A common design is to instrument applications with OpenTelemetry SDKs or agents, send telemetry through an OpenTelemetry Collector, and choose an OTLP-capable backend. The backend might be Jaeger, Tempo, Elastic APM, SigNoz or another option that meets your requirements. Grafana’s Application Observability ecosystem describes Alloy as a Collector, with dashboards and tools around the collected data; Tempo’s documentation likewise recommends a collector to receive application traces and forward them to the backend.
- Define the signals and questions. Decide whether the first operational need is tracing, request and error analysis, service topology, metrics, logs, or correlation among them. Include the queries responders need during an incident.
- Check instrumentation before building dashboards. Confirm that SDKs or agents cover the languages, frameworks and runtime versions in your estate. Decide how application context, such as service identity, will be represented consistently.
- Choose the pipeline role. If you need a separate collection and routing layer, evaluate the OpenTelemetry Collector and the relevant configuration for your telemetry flow. Grafana’s Alloy is another Collector path in its Application Observability ecosystem.
- Choose a backend that matches the scope. A tracing backend is not automatically a complete logs-and-metrics platform. Verify storage, retention, sampling, query behavior and integrations before standardizing on one.
- Run a representative evaluation. Use a small set of real services and operational questions. Check whether traces arrive, whether responders can find the relevant request or service, and whether the storage and retention design is workable for your team.
What to test before committing
- Signal coverage: Can the candidate give responders the traces, metrics, logs and error context they actually use, or will separate systems still be required?
- Instrumentation: Are the required languages and frameworks supported by a current agent or OpenTelemetry path? Is the instrumentation maintainable across upgrades?
- Data lifecycle: Identify where data is stored, how retention is controlled, and how sampling affects the ability to investigate low-frequency failures.
- Queries and workflow: Test searches and correlations that mirror incidents, not only a demonstration dashboard.
- Operations: Account for deployment, upgrades, storage, scaling and access management for a self-hosted installation. “Open source” does not mean “no operational work.”
- Existing systems: Check fit with Grafana, Elastic, Kubernetes and your cloud environment instead of assuming integrations will meet your needs.
No cross-tool market-share, adoption, performance or cost figures are established by the official materials summarized here. A controlled benchmark would need to specify the workload, retention, sampling, storage, deployment and query being measured; without that, claims that one is fastest or cheapest are not useful evidence.
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Troubleshooting common evaluation problems
Telemetry does not appear in the backend
Trace the path one boundary at a time: application instrumentation, Collector or other receiving layer, export configuration, and backend ingestion. Confirm that the application is producing the signal you expect and that the destination is the one configured. A pipeline can route data; it cannot display data if the backend or its ingestion path is missing.
Some services appear, but traces are disconnected
Check that services are instrumented consistently and that the trace context is carried across the calls you expect to correlate. Validate the same end-to-end request across the services involved before comparing user interfaces. The exact setup depends on the instrumentation and framework in use.
Tracing works, but responders still lack context
This may be a scope mismatch, not a failed tracing backend. Traces do not automatically provide a complete logs-and-metrics APM experience. Decide whether to add correlated logs and metrics, select a broader platform, or keep the tracing backend and assemble the other components around it.
Storage or queries become a concern
Revisit the retention, sampling, storage and query assumptions using the actual evaluation workload. These choices affect what can be investigated and how the backend is operated. The project descriptions do not establish performance under your workload, so do not infer a capacity target from a feature list.
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Check current runtime, framework and release support for the exact application. A project being open source or OTLP-native does not prove that every agent, library or framework version is supported. If coverage is insufficient, evaluate an OpenTelemetry instrumentation path or another candidate before expanding deployment.
Can open-source APM replace a commercial service?
It can replace some or all of a commercial APM workflow, but “replace” depends on what the team currently uses. A tracing backend may cover distributed traces while leaving logs, metrics, alerting or profiling in other tools. A broader platform may reduce that stitching, but the team still needs to validate instrumentation, retention, query workflows and operational effort. The available descriptions do not establish feature-for-feature parity with any particular commercial service.
Compare the full system you would run—not only the license or the backend name. Include the Collector or agent layer, storage, retention, integrations, dashboards, upgrades and the work required to keep the stack reliable.
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