LLM Observability — Traces, Metrics and Logs for Large Language Models
Monitor every LLM call in production — latency, cost, hallucination rate, eval scores — with OpenTelemetry-native distributed tracing.
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OTel
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LLM observability
LLM observability is the practice of tracing, metering and evaluating large language model calls in production — capturing prompts, completions, latency, token cost and evaluation scores to debug issues and optimize performance. As AI systems evolve to multi-agent architectures, the industry is broadening the term to AI observability.
LLM observability — the foundation of production AI monitoring
Instrument once, observe everything
One OTel SDK, one OTLP exporter. No proprietary agents or middleware sitting in the critical request path.
Eval scores on production traffic
Faithfulness, answer relevancy, and hallucination rates measured on live requests — not just curated test sets.
Compliance evidence built in
Structured audit logs formatted for HIPAA, EU AI Act, DORA, and MAS FEAT. No manual export, no post-processing.
How the tooling evolved from 2022 to today
LLM observability started in 2022 when teams first deployed GPT-based applications to production and discovered that traditional APM could not explain why a completion was wrong or expensive. The first generation of tools — Helicone, PromptLayer — focused on logging prompts and completions. The second generation — Langfuse, Braintrust — added OTel traces and eval scoring.
What LLM observability covers end-to-end
Today LLM observability covers: distributed traces (OTel spans with gen_ai.* attributes), token-level metrics (per-model cost, context window usage, latency breakdown), evaluation scores (RAGAS faithfulness, hallucination detection, answer relevancy), prompt and completion logging (with PII redaction for regulated use cases), and alert thresholds on degrading metrics.
Where the category is heading
The terminology is shifting: "LLM observability" is being subsumed by "AI observability" and "agent observability" as systems expand beyond single-model calls. The buyer intent is converging — teams searching "LLM observability" today will be searching "AI observability" by 2027. This page covers LLM-level tracing; see AI observability guide for the broader category.
Core LLM observability capabilities
Everything your team needs to instrument, evaluate, and audit AI systems in production — with evidence that satisfies your compliance requirements.
Distributed Traces
OTel-native span tracing with gen_ai.* semantic conventions. Every LLM call gets a span ID, model, token counts, latency, and outcome.
Token-Level Cost Tracking
Per-request, per-model cost in USD. Aggregate by user, team, feature, or date range for budget governance.
Eval Scoring
RAGAS faithfulness, answer relevancy, hallucination rate. Run as inline eval during inference or as batch eval against stored traces.
Prompt and Completion Logging
Full prompt and completion capture with configurable PII redaction (PHI patterns, PII patterns, custom regex).
Latency Profiling
Time to first token, end-to-end latency, p50/p95/p99 breakdowns. Identify slow models, slow retrievers, and slow tool calls.
Common questions, answered
Answers to the most common questions about this regulation, what it requires, and how AI observability helps you meet it.
What is LLM observability?
LLM observability is the practice of collecting traces, metrics, logs, and evaluation scores from large language model calls in production. It answers: why was this response slow, wrong, or expensive — with evidence from production data, not reproduction in a test environment.
How does LLM observability work with OpenTelemetry?
OTel provides the instrumentation standard. You add the OTel SDK to your LLM application, annotate LLM calls with gen_ai.* semantic conventions (model, tokens, cost), and configure the OTLP exporter to send spans to your observability backend (Perimattic, Langfuse, Datadog). This approach is framework-agnostic — it works with any LLM provider and any orchestration framework.
What's the difference between LLM observability and AI observability?
LLM observability covers single-model calls: prompt → completion traces, token metrics, eval scores. AI observability is the broader term that includes multi-agent orchestration traces, RAG pipeline tracing, tool-call graphs, and governance compliance layers. LLM observability is the foundation; AI observability extends it upward.
Which LLM providers does Perimattic AI Suite support for observability?
Any provider that can be instrumented with OTel: OpenAI (GPT-4o, o3, o4-mini), Anthropic Claude (Opus, Sonnet, Haiku), AWS Bedrock (Claude, Llama, Titan, Mistral), Azure OpenAI, Google Vertex AI Gemini, Mistral, and Aleph Alpha. Provider support is additive — as new models ship, the OTel semantic conventions cover them automatically.
How much does LLM observability cost to run?
The observability overhead is minimal — OTel spans are small (a few KB per call) and the OTLP export is asynchronous and does not block the critical path. At scale (millions of LLM calls per day), storage is the main cost driver. Perimattic AI Suite supports configurable trace sampling and retention policies to control storage costs.
Related pages
Dig deeper into the topics that matter most for your AI observability stack and compliance posture.
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