Agentifact assessment — independently scored, not sponsored. Last verified Apr 6, 2026.
Grafana LLM Observability
Grafana plugin for LLM application metrics. Integrates with OpenTelemetry traces from LangChain, OpenAI, and Anthropic — visualizes token usage, latency histograms, error rates, and cost trends in Grafana dashboards.
Viable option — review the tradeoffs
You're running LLM applications (RAG pipelines, agents, chatbots) and need to understand token consumption, API latency, and costs across multiple models and providers—but standard API monitoring doesn't capture prompt/response details or hallucination risk.
Rich trace visibility into LLM call sequences, accurate token/cost tracking, and fast anomaly detection. Dashboard setup is straightforward via pre-built OpenLIT templates. Expect 5–15 minute instrumentation for greenfield apps; legacy apps may need adapter work. The LLM plugin's AI features (summaries, explanations) are convenient but depend on OpenAI availability and add minor latency.
You're managing multiple LLM models and providers (OpenAI, Anthropic, custom endpoints) and need to compare performance, cost-per-token, and error rates across them to optimize spend and model selection.
Clear visibility into which models are most expensive and slowest. Cost dashboards update in near-real-time. Segmentation by platform and request type is granular. Caveat: you must instrument consistently across all providers; gaps in trace coverage will skew comparisons.
Hallucination and quality detection requires manual setup
While Grafana's AI observability docs mention hallucination detection and toxicity checks, the LLM plugin itself does not perform these evaluations automatically. You must integrate a separate evaluation framework (e.g., LangSmith, custom validators) and export those signals as traces or metrics to Grafana. The plugin visualizes what you send it; it does not generate quality assessments.
OpenTelemetry instrumentation and trace export pipeline
Grafana LLM Observability is a visualization and analysis layer; it requires upstream trace collection. You must instrument your LLM app with OpenTelemetry SDKs (via LangChain, LlamaIndex, or manual spans) and configure a collector to export traces to Grafana Cloud. Without this, there is no data to visualize.
OpenAI API key exposure and data sharing via LLM plugin
The Grafana LLM plugin (for incident summaries, panel explanations, etc.) requires you to approve data sharing with OpenAI's API. This means Grafana will send log excerpts, flame graphs, and error details to OpenAI for processing. If your logs or traces contain sensitive data (PII, secrets, proprietary prompts), this is a compliance risk. Disable the LLM plugin features if data residency or confidentiality is a hard requirement; the core observability (traces, metrics, dashboards) works without it.
Trust Breakdown
What It Actually Does
Monitor your AI application's performance and costs in real time. This Grafana plugin displays token usage, response times, error rates, and spending trends from your LLM integrations in one dashboard.
Grafana plugin for LLM application metrics. Integrates with OpenTelemetry traces from LangChain, OpenAI, and Anthropic — visualizes token usage, latency histograms, error rates, and cost trends in Grafana dashboards.
Score Breakdown
Protocol Support
Capabilities
Governance
- permission-scoping
- audit-log
- rate-limiting
- backend-proxying
- encryption-in-transit
- encryption-at-rest