Directory Observability & MonitoringAPM & Distributed TracingElastic APM

Elastic APM

About Elastic APM

Elastic APM ingests OpenTelemetry-native traces, logs, and metrics from third-party OTel tools or custom collectors. It extends OTel semantic conventions with a unified schema to correlate traces with logs and metrics from any source. It supports native OTel schematic conventions and W3C trace context so telemetry data is not locked into a proprietary format. It supports multiple programming languages and frameworks for modern or legacy applications.

Products

Elastic Distributions of OpenTelemetry (EDOT): A supported OTel distribution with auto-instrumentation for several languages and platforms.
Elastic AI Assistant: Helps investigate transaction latency issues, application errors, and failures by suggesting next steps.

Used for

Tracking requests across a stack using distributed tracing
Correlating latency, error, and failure symptoms to possible causes
Tracing large language models to optimize GenAI applications
Monitoring mobile application performance on iOS and Android

From Elastic APM'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 Elastic APM. 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

Other APM & Distributed Tracing Vendors

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Airbrake

Alibaba Cloud Application Real-Time Service

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