Agentifact assessment — independently scored, not sponsored. Last verified Apr 2, 2026.
New Relic AI Monitoring
AI Monitoring in New Relic APM that traces LLM calls end-to-end, tracks token consumption, response times, and model costs alongside application performance. Auto-instruments OpenAI, Anthropic, and Bedrock integrations.
Viable option — review the tradeoffs
You can't see end-to-end performance of LLM calls in your app, missing token costs, response times, and how they impact overall system health.
Instant dashboards for tokens, latency, errors, and costs; reliable for production but tied to New Relic ecosystem quirks like NRQL learning curve.
Agentic AI meshes hide tool calls, inter-agent errors, and bottlenecks, slowing root cause analysis.
Granular views of agent calls and errors accelerate troubleshooting; strong for complex meshes but best with full New Relic stack.
ML models drift undetected in production, causing silent failures without correlated app insights.
Quick drift detection and alerts reduce noise by 27%; effective but requires ongoing New Relic usage for max value.
New Relic Platform Account
AI Monitoring is embedded in New Relic APM; standalone use impossible without their full observability platform.
Limited Model Support
Auto-instruments only OpenAI, Anthropic, Bedrock; custom LLMs need manual setup.
Trust Breakdown
What It Actually Does
Tracks AI model API calls alongside your app performance, showing token usage, response times, and costs for OpenAI, Anthropic, and Bedrock in one dashboard.
AI Monitoring in New Relic APM that traces LLM calls end-to-end, tracks token consumption, response times, and model costs alongside application performance. Auto-instruments OpenAI, Anthropic, and Bedrock integrations.
Fit Assessment
Best for
- ✓ai-monitoring
- ✓observability
- ✓apm
- ✓infrastructure-monitoring
Connection Patterns
Blueprints that include this tool:
Score Breakdown
Protocol Support
Capabilities
Governance
- pii-masking
- audit-log