{"name":"Grafana","slug":"grafana","category":"observability","type":"hybrid","website":"https://grafana.com","pricing":"freemium","pricing_tiers":["Free (self-hosted OSS)","Free cloud (10k metrics)","$29/mo Pro","Custom Enterprise"],"open_source":true,"self_hosted":true,"sdk_languages":["python","javascript","go","java"],"frameworks":[],"agent_features":{"llm_tracing":false,"cost_tracking":false,"evaluation":false,"prompt_management":false,"real_time_monitoring":true},"compliance":["soc2","hipaa","gdpr"],"best_for":"Infrastructure dashboards and alerting — best paired with Prometheus/Loki/Tempo for a fully open-source observability stack","limitations":"No native LLM tracing; requires additional tooling (Langfuse, OpenTelemetry) for AI-specific observability; steep learning curve for the full LGTM stack","verified_by":"editorial","last_verified":"2026-04-28","source_urls":{"docs":"https://grafana.com/docs","pricing":"https://grafana.com/pricing"},"feature_labels":{"llm_tracing":"Trace LLM calls, tool invocations, and agent reasoning steps end-to-end","cost_tracking":"Track token usage and cost per request, per agent run, and per model","evaluation":"Score agent outputs against test datasets with automated evaluators","prompt_management":"Version, manage, and A/B test prompts in production","real_time_monitoring":"Live dashboards and alerting for agent performance metrics"},"comparisons":[{"slug":"datadog-vs-grafana","title":"Datadog vs Grafana","vs":"datadog"},{"slug":"grafana-vs-helicone","title":"Grafana vs Helicone","vs":"helicone"},{"slug":"grafana-vs-langfuse","title":"Grafana vs Langfuse","vs":"langfuse"},{"slug":"grafana-vs-langsmith","title":"Grafana vs LangSmith","vs":"langsmith"}],"body":"# Grafana\n\nGrafana is the dominant open-source dashboarding and visualization platform. It doesn't provide LLM-specific tracing natively, but it's the go-to choice for infrastructure observability — metrics, logs, and traces via the Prometheus/Loki/Tempo stack (often called LGTM).\n\nFor AI agent teams, Grafana is typically used alongside a dedicated LLM observability tool. It handles the infrastructure layer (container metrics, API latency, error rates) while something like Langfuse handles the LLM-specific tracing."}