New Relic AI

New Relic AI transforms application performance monitoring and observability through advanced artificial intelligence and machine learning capabilities. The platform ingests telemetry data from across your entire technology stack—applications, infrastructure, containers, serverless functions, logs, and browser sessions—and applies AI-driven analysis to surface anomalies, correlate incidents, and suggest root causes automatically. Unlike traditional monitoring tools that rely on static thresholds and manual configuration, New Relic AI continuously builds dynamic baselines from billions of data points and detects meaningful deviations before they escalate into customer-facing outages.

Designed for DevOps engineers, SREs, platform teams, and development organizations of all sizes, New Relic AI serves as a unified observability layer that eliminates the need to stitch together multiple point solutions. The platform's AI assistant, New Relic AI (NRAI), allows users to query their telemetry data using natural language, build dashboards conversationally, and get plain-English explanations of complex system behaviors. This dramatically lowers the barrier for on-call engineers and less experienced team members to diagnose production issues quickly.

New Relic operates on a consumption-based pricing model with two billing dimensions: data ingest and user seats. The free tier includes 100 GB of monthly data ingest, one full-platform user, and access to all 30+ observability capabilities—making it one of the most generous free offerings in the monitoring space. Paid tiers add volume discounts, advanced compliance certifications, and premium support. With over 700 pre-built integrations spanning AWS, Azure, GCP, Kubernetes, major programming languages, CI/CD tools, and third-party services, New Relic connects to virtually any component in a modern technology stack with minimal configuration.

The platform's AI capabilities extend beyond simple anomaly detection. New Relic AI correlates related incidents across services, infrastructure, and deployments to automatically identify probable root causes. It understands application dependency maps and business impact, prioritizing alerts based on actual customer experience rather than raw technical metrics. The AI engine continuously learns from system behavior patterns and operator feedback to improve detection accuracy and reduce false positives over time, adapting to each organization's unique operational patterns.