Guide
What is LLM Observability?
A complete guide to monitoring large language models in production
LLM observability is the practice of monitoring, tracking, and analyzing large language model behavior in production environments. As organizations deploy AI at scale, understanding what your models are doing—and catching problems before users do—becomes essential.
Why LLM Observability Matters
Traditional application monitoring tracks uptime, errors, and latency. LLM observability goes further because AI models introduce unique risks:
- Hallucinations: Models confidently state false information
- Prompt injection: Malicious inputs manipulate model behavior
- PII exposure: Models may leak sensitive data in responses
- Cost overruns: Token usage can spike unexpectedly
- Model drift: Behavior changes over time without code changes
What is LLM observability?
LLM observability is the practice of monitoring, tracking, and analyzing large language model behavior in production environments. It includes logging inputs and outputs, measuring latency and costs, detecting hallucinations, identifying safety risks, and maintaining audit trails for compliance.
Why is LLM observability important?
LLM observability is critical because AI models can produce unpredictable outputs including hallucinations, toxic content, or PII exposure. Without observability, organizations cannot detect these issues, optimize costs, maintain compliance, or improve model performance over time.
What should LLM observability track?
LLM observability should track: input prompts and output responses, latency and token usage, cost per request, hallucination and accuracy scores, safety classifications (toxicity, PII, prompt injection), model drift over time, and user feedback signals.
Key Components of LLM Observability
A complete LLM observability stack includes:
Event Logging — Capture every inference request with full context: the prompt, retrieved documents (for RAG), model response, and metadata like latency and token counts.
Safety Classification — Automatically analyze outputs for risks including hallucinations, toxicity, PII exposure, and prompt injection attempts.
Drift Detection — Monitor for changes in model behavior over time. Risk score distributions, latency patterns, and error rates should remain stable.
Audit Trails — Maintain immutable logs for compliance. Regulated industries require proof of AI governance.
LLM Observability vs Traditional APM
Application Performance Monitoring (APM) tools like Datadog and New Relic excel at infrastructure metrics but lack LLM-specific capabilities:
| Capability | Traditional APM | LLM Observability |
|---|---|---|
| Latency tracking | Yes | Yes |
| Error rates | Yes | Yes |
| Hallucination detection | No | Yes |
| PII detection | No | Yes |
| Prompt injection detection | No | Yes |
| Compliance reports | No | Yes |
Getting Started
Implementing LLM observability typically involves adding an SDK to your application that logs inference events to a monitoring platform. The platform then classifies each event for risks and provides dashboards, alerts, and compliance reports.
DriftRail provides LLM observability with built-in safety classification, drift detection, and compliance reporting for regulated industries.