LogRocket
About LogRocket
LogRocket's product identifies issues affecting users and provides context for resolving them. It can be installed on websites or mobile apps. It integrates with other tools in a company's technology stack.
Products
- Product Analytics: Builds conversion funnels, path analysis, and timeseries to understand user behavior.
- Issues: AI proactively identifies technical and UX issues impacting the business.
- UX Analytics: Visualizes how users experience the app at individual and aggregate levels.
- Error Tracking: Shows the most frequent errors, network failures, and crashes affecting users.
Used for
- Surfacing user struggle
- Optimizing conversion and adoption
- Drilling down with session replays to understand individual behavior
- Viewing DOM playback, console and network logs, errors, and performance data
- Creating charts and dashboards to measure performance impact and changes over time
Who it is for
- Used in retail and eCommerce for engineering purposes.
- Used by software companies for product and UX purposes.
- Used in healthcare for engineering purposes.
From LogRocket'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 LogRocket. 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 β 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
8%
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 β if self-hosted β the operational burden of running and upgrading the backend at high cardinality
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Quick Facts
logrocket.com Pricing pageCategoryObservability & Monitoring
SubcategoryAPM & Distributed Tracing
DeploymentSelf-hosted
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