LLM Observability vs Traditional Monitoring: Key Differences

Traditional Application Performance Monitoring (APM) tools like Datadog and New Relic are essential for infrastructure monitoring. But they weren't designed for the unique risks of AI applications.

What is the difference between LLM observability and traditional monitoring?

Traditional monitoring (APM) tracks infrastructure metrics like latency, errors, and throughput. LLM observability adds AI-specific capabilities: hallucination detection, safety classification, PII detection, prompt injection monitoring, compliance reporting, and model drift detection. APM tells you if your service is up; LLM observability tells you if your AI is behaving safely.

Comparison

Capability	Traditional APM	LLM Observability
Latency tracking	Yes	Yes
Error rates	Yes	Yes
Distributed tracing	Yes	Some
Hallucination detection	No	Yes
PII detection	No	Yes
Prompt injection detection	No	Yes
Toxicity classification	No	Yes
Compliance reports	No	Yes
Model drift detection	No	Yes

Using APM with LLM Observability

Can I use Datadog or New Relic for LLM monitoring?

Datadog and New Relic can track LLM latency, error rates, and costs, but they lack AI-specific features like hallucination detection, safety classification, and compliance reporting. You can use them alongside LLM observability tools—APM for infrastructure, LLM observability for AI safety and quality.

Key Metrics

What metrics should I track for LLM applications?

Track both operational and AI-specific metrics. Operational: latency, token usage, error rates, costs. AI-specific: hallucination rate, risk score distribution, PII detection rate, prompt injection attempts, toxicity flags, and model drift indicators. AI metrics require specialized classification that APM tools don't provide.

Do You Need Both?

Do I need both APM and LLM observability?

For production LLM applications, yes. APM handles infrastructure monitoring, distributed tracing, and alerting on system health. LLM observability handles AI-specific risks, safety classification, and compliance. Many teams use Datadog/New Relic for infrastructure and a specialized tool like DriftRail for AI safety.

DriftRail supports OpenTelemetry export, allowing you to send LLM observability data to your existing APM tools like Datadog, Grafana, or Jaeger for unified dashboards.