Directory Observability & MonitoringAPM & Distributed TracingNew Relic

New Relic

About New Relic

New Relic's platform brings together telemetry, operational context, AI, and business data. It unifies metrics, events, logs and traces in a single data platform. It correlates applications, infrastructure, logs, digital experiences, AI systems, and business outcomes. It supports open standards for telemetry ingestion.

Products

APM: Connects application performance to customer experience and revenue.
Digital Experience Monitoring: Uses AI to pinpoint session friction and UX flaws.
AI and Agentic Monitoring: Monitors every model and agent interaction in real time.
Infrastructure Monitoring: Manages cloud-native and Kubernetes environments with infrastructure health tied to applications.
Logs: Allows querying of log data in place to scale logging strategy.
Security: Prioritizes and resolves vulnerabilities using production impact and AI reasoning.

Used for

Tracing agent reasoning to code execution to identify where logic drifts from intended behavior
Gaining real-time visibility into multi-cloud and Kubernetes spend
Controlling behavior and token usage across an AI stack
Identifying friction points in session replay using AI
Delivering observability insights to AI agents where engineers work

Who it is for

Used in the retail/commerce industry.
Used in the media/entertainment industry.
Used in the telecommunications industry.

From New Relic'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 New Relic. 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

Alibaba Cloud Application Real-Time Service

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