Directory Observability & MonitoringAPM & Distributed TracingStagemonitor

Stagemonitor

Open Source

About Stagemonitor

Stagemonitor is an open source solution to application performance monitoring for java server applications. It can be deployed on a company's own datacenter so that no data is sent to the outside world. It uses the Open Tracing API to correlate requests in a distributed system. It is designed to be used in development, QA and production. Users can write their own plugins, use third party plugins, track their own metrics and create or customize dashboards.

Products

Request Dashboard: A dashboard showing statistical distribution of response times, throughput and error rate.
JVM Dashboard: A dashboard showing heap utilisation, garbage collection behaviour and CPU utilisation.
EhCache Metrics: A dashboard showing hit-rate, cache size, get performance, throughput and expiries of caches.

Used for

Viewing the methods invoked during a request
Identifying bottlenecks of a request via the method call tree
Analyzing errors in production and web analytics

Who it is for

Used by developers, for example through instant feedback on poorly performing parts of the application.
Used in operations for monitoring clustered environments.
Brings together development and operations teams.

From Stagemonitor's own website, read 2026-10-23. How we build listings.

How to evaluate APM & Distributed Tracing

This is how the CIOPages Research Team evaluates this category. It is not an assessment of Stagemonitor. It comes from our Observability & APM Platforms buyer guide.

22%

Telemetry Coverage & Correlation

Unified metrics, traces, logs, real-user monitoring, synthetics, and continuous profiling; automatic correlation across signals; service maps and topology; Kubernetes, container, serverless, and cloud-provider auto-discovery; breadth and quality of the integration catalog

22%

Cost Control, Cardinality & Data Governance

Pricing-unit transparency and overage terms; high-cardinality handling without runaway cost; ingest-time filtering, sampling, aggregation, and tiered retention; a native or compatible telemetry pipeline; per-team budgets, quotas, and chargeback visibility so spend is governable, not just observable

20%

Distributed Tracing & APM Depth

End-to-end trace correlation across services and async boundaries, tail-based and adaptive sampling, code-level and method-level profiling, error tracking and exception grouping, database and dependency visibility, and frictionless OpenTelemetry trace ingestion alongside any native agent

16%

AIOps, Anomaly Detection & Root Cause

Quality of automated anomaly detection and alert noise reduction, causal versus correlative root-cause analysis, forecasting and capacity signals, and emerging agentic remediation — judged on accuracy during a real incident, not on the marketing name of the AI engine

12%

OpenTelemetry & Openness

Depth of native OTel support (traces, metrics, logs) versus a proprietary agent; whether collection is genuinely vendor-neutral or quietly locks you in; query-language portability; export and data-egress freedom; and how much re-instrumentation a future migration would actually require

Scale, Reliability & Operability

Proven performance at your peak event and series volumes, multi-region availability and platform SLA, data-retention options, RBAC and SSO, and — if self-hosted — the operational burden of running and upgrading the backend at high cardinality

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