Directory Observability & MonitoringAPM & Distributed TracingFaro (Grafana)

Faro (Grafana)

Open Source

About Faro (Grafana)

Grafana Faro is a project for frontend application observability. The Grafana Faro Web SDK is an open source JavaScript agent embedded in web applications to collect real user monitoring data. Faro was started at Grafana Labs and announced in 2022. Collected frontend data can be correlated with backend and infrastructure data in the Grafana Labs LGTM stack. Grafana Labs leads development of the Faro project and builds support for it into Grafana.

Products

Grafana Faro Web SDK: A JavaScript agent embedded in web applications to collect performance metrics, logs, exceptions, events, and traces.

Used for

Monitoring web application performance by gathering web vitals metrics
Capturing unhandled errors and rejected promises to help discover and troubleshoot errors
Tracking user activity, such as user sessions and key events as well as feature usage, user journeys, and KPIs
Collecting frontend logs to gain additional context into errors, user activities, and application state
Capturing frontend traces via OpenTelemetry-JS integration

From Faro (Grafana)'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 Faro (Grafana). 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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Airbrake

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