Grafana vs Helicone

Grafana and Helicone approach agent observability from opposite directions. Helicone is an AI-specific proxy that automatically logs LLM requests with cost tracking and session grouping. Grafana is a general-purpose observability platform that can ingest LLM data via OpenTelemetry. It has no LLM-specific features. Helicone wins on agent-relevant observability out of the box. Grafana wins on self-hosting and infrastructure monitoring.

Where Helicone wins

• Automatic LLM request logging with zero-code proxy integration. Helicone captures every LLM request by routing calls through its proxy gateway—change the base URL and all requests are logged with prompts, completions, token counts, latency, and cost. No SDK, no decorators, no manual instrumentation. Supports 100+ models across OpenAI, Anthropic, Google, Groq, and Vertex AI. Grafana requires custom OpenTelemetry instrumentation to capture LLM-specific data (prompts, completions, tokens). The developer must emit custom span attributes, configure Tempo for ingestion, and build dashboards to visualize them.

• Session-based agent chain grouping with cost attribution. Helicone Sessions group related requests into hierarchical traces using path-based headers. The full agent workflow is shown from initial input to final output. Per-request cost tracking across all supported providers provides cost attribution by model, user, and session. Grafana has no built-in cost tracking, no token counting, and no LLM-aware session grouping—building equivalent capabilities requires custom application metrics and custom dashboards.

• Score ingestion from external evaluators. Helicone accepts evaluation scores via API from external systems — RAGAS, LLM-as-judge evaluators, and custom scorers — attaching them to specific requests. Scores are aggregated in dashboards for quality monitoring. Grafana has no evaluation score model and no mechanism for attaching quality scores to traces.

Where Grafana wins

• Fully self-hostable open-source infrastructure monitoring. Grafana's LGTM stack is entirely open-source and self-hostable with no feature restrictions. For agent systems handling sensitive data, all observability data stays within the organization's infrastructure. Helicone is cloud-hosted with an enterprise on-premises option. The open-source self-hosted path requires the Enterprise tier. Grafana's self-hosting is free and unrestricted.

• Full infrastructure observability alongside agent data. Grafana provides host metrics (CPU, memory, disk, network), container monitoring, database query performance, and log aggregation. This infrastructure context explains why agent latency spiked or tool calls failed. Helicone captures LLM requests only—when a tool-call failure is caused by database timeout or container OOM, Helicone has no visibility into the infrastructure layer.

The agentic difference

Helicone provides the LLM observability that agent developers need with minimal effort: automatic request logging, cost tracking, session grouping, and score ingestion. Deployment requires changing a base URL. Grafana provides the infrastructure observability that operations teams need: host metrics, logs, traces, and dashboards. Deployment requires significant configuration.

For agent teams that want LLM request visibility and cost tracking running today, Helicone delivers that immediately. For teams that need to correlate LLM behavior with infrastructure state, Grafana provides that context. LLM-specific observability must be built custom. Most production agent deployments benefit from both: Helicone for LLM request observability and Grafana for infrastructure monitoring.

When to pick which

• Pick Helicone when the priority is automatic LLM request logging with cost tracking across multiple providers, and the team wants proxy-based integration with no custom instrumentation — especially for agent architectures where cost visibility and per-request attribution are the primary observability needs.

• Pick Grafana when the primary requirement is infrastructure monitoring for the systems running agents, self-hosting is mandatory, or the team already operates the Grafana stack and wants to extend it for LLM workloads via custom OpenTelemetry instrumentation.