Directory Observability & MonitoringAPM & Distributed TracingOPENMARU COP

OPENMARU COP

About OPENMARU COP

OPENMARU Cloud APM is a monitoring solution for the performance and status of business applications. The product is described as optimized for cloud-native and hybrid environments. It deploys agents on application servers to monitor page request response times and database query response times. The company, Openmaru, is described as a company specializing in open-source technology that provides killer services and solutions for building IT systems on an open-source basis. Openmaru is a Red Hat Specialty Partner.

Products

OPENMARU Cloud APM: Monitors the performance and status of business applications in real time.
OpenShift & Kubernetes 모니터링: Monitors Docker containers and WAS running on Docker containers.
WEB / WAS 모니터링: Monitors WEB/WAS internally and analyzes availability and performance.
URL 모니터링: Monitors a URL by executing pre-set rules to determine the presence of failures.
OPENMARU Cloud APM for CUBRID: Provides a real-time dashboard, CAS status, broker status, analysis and statistics tools for CUBRID DB instances.

Used for

Detecting service failures
Shortening the time needed to identify the cause of a failure
Identifying the cause of service load
Monitoring Docker containers and WAS operating on them
Managing history of DBMS performance-related information

Who it is for

Small and medium-sized businesses, large enterprises, and public institutions are stated as users of the product.

From OPENMARU COP's own website, read 2026-10-24. 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 OPENMARU COP. 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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