Directory Observability & MonitoringAPM & Distributed TracingRaygun

Raygun

About Raygun

Raygun provides software for monitoring and diagnosing errors and performance issues in applications. The platform includes Crash Reporting, Real User Monitoring, and Application Performance Monitoring (APM). It also offers AI Error Resolution, which provides error context to a chosen large language model. Raygun supports integrations with tools such as Slack, GitHub, Jira, and Bitbucket.

Products

Crash Reporting: Diagnoses and resolves issues users experience by pinpointing root causes.
Real User Monitoring: Provides performance data to identify bottlenecks and diagnose issues for mobile and web applications.
APM: Enables monitoring and management of back-end issues by capturing the lifecycle of incoming web requests.
AI Error Resolution: Auto-prompts a chosen LLM with error context from the stack trace, environment, and affected code.
Deployment Tracking: Monitors every deployment in real time to identify new issues or regressions.
Dashboards: Visualizes critical error and performance data with customizable displays.

Used for

Tracking client-side and server-side errors across desktop and mobile
Identifying server-side performance issues and viewing transaction details
Monitoring deployments in real-time to identify new issues or regressions
Sorting errors by affected user count to find those with the biggest impact
Setting alerts for drops in page or XHR performance

Who it is for

Tech leads
CX experts
Performance engineers
Companies of all sizes, from startups to Fortune 500 organizations

From Raygun'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 Raygun. 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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