Grafana vs Langfuse

Grafana and Langfuse are both open-source and self-hostable. They serve different observability needs. Langfuse is built for LLM application observability with agent trace capture, evaluation, prompt management, and cost tracking. Grafana is a general-purpose observability platform with no LLM-specific features. Langfuse wins on all three agentic dimensions.

Where Langfuse wins

• Purpose-built LLM tracing with nested agent chain visualization. Langfuse captures traces with nested observations: spans, generations, and events. The full agent reasoning chain is shown: orchestrator decisions, tool calls, retrieval steps, and LLM calls with prompts, completions, token counts, and latency. Sessions group multi-turn conversations. User tracking attributes traces to specific users. 90+ integrations auto-instrument LangChain, LlamaIndex, OpenAI, Anthropic, Vercel AI SDK, LiteLLM, CrewAI, and more. Grafana can ingest OpenTelemetry traces via Tempo. There are no LLM-specific trace visualizations, no auto-instrumentation for agent frameworks, and no pre-built views for prompts, completions, or token counts. Building equivalent agent chain visibility requires custom instrumentation, custom span attributes, and custom dashboards.

• Evaluation pipelines and prompt management. Langfuse provides LLM-as-judge evaluators, human annotation queues, custom scoring via API, datasets for systematic testing, and experiments for A/B comparison. All attach to production traces. Prompt management with versioning, playground testing, deployment labels, and runtime SDK fetching enables prompt iteration without code changes. Grafana has no evaluation capability and no prompt management feature. These workflows—"is the agent's output correct?" and "which prompt version performs better?"—are entirely outside Grafana's scope.

• Cost tracking with per-request and per-model attribution. Langfuse tracks token usage and calculates cost per trace and generation. Dashboards show cost by model, user, and time period. Cost data is captured automatically through framework integrations. Grafana can display custom cost metrics if the application emits them. It provides no built-in token counting, cost calculation, or provider cost mapping.

Where Grafana wins

• Full observability stack with infrastructure monitoring. Grafana's LGTM stack (Loki, Grafana, Tempo, Mimir) provides logs, traces, metrics, and dashboards for the entire infrastructure—not just LLM calls. When an agent's latency spikes, Grafana can correlate with host CPU, database query time, network latency, and container resource utilization. Langfuse is LLM-observability-only—it traces agent logic but has no visibility into the infrastructure running it. For production agent systems, infrastructure-level observability is a separate requirement that Langfuse does not address.

• Unlimited customization and no vendor lock-in. Grafana's OpenTelemetry-native architecture accepts any span, metric, or log in any format. Custom dashboards can visualize any data combination. The LGTM stack is fully open-source with no feature restrictions on self-hosted deployments. Langfuse is also open-source and self-hostable, but its data model is optimized for LLM workloads — non-LLM traces and infrastructure metrics are outside its scope.

The agentic difference

The comparison is between a dedicated agent observability platform and a general infrastructure observability platform. Langfuse provides what agent developers need out of the box: trace agent reasoning chains, evaluate output quality, iterate on prompts, and track costs. Grafana provides the infrastructure to build those capabilities from scratch. The development effort is substantial—custom instrumentation, custom dashboards, custom evaluation logic, and custom cost tracking.

For teams that need both agent observability and infrastructure monitoring, the answer is typically both tools: Langfuse for LLM-specific tracing, evaluation, and prompt management; Grafana for infrastructure metrics, logs, and system-level dashboards. They are complementary for most agent deployments.

When to pick which

• Pick Langfuse when the team needs agent chain tracing, LLM output evaluation, prompt management, and cost tracking — the core agent observability workflow — with minimal setup and self-hosting if required.

• Pick Grafana when the primary requirement is infrastructure-level observability for the systems running agents (host metrics, container logs, database performance, network traces), and LLM-specific tracing is handled by a dedicated tool or built custom using OpenTelemetry.