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Logfire vs Grafana

Grafana is a powerful open-source visualization platform that works with various data sources including Tempo (traces), Loki (logs), and Prometheus (metrics). Logfire is a managed observability platform with integrated visualization. Both can display your observability data, but they take very different approaches.

Quick Comparison

Feature Logfire Grafana Stack
Setup One-line: logfire.configure() Deploy Tempo, Loki, Prometheus, configure each
Configuration Minimal Extensive dashboard building required
AI/LLM Support First-class, purpose-built panels Manual dashboard creation
Live View Built-in real-time view Complex to configure reliably
Query Language SQL (Postgres-compatible) PromQL, LogQL, TraceQL (different for each)
Maintenance Zero Ongoing tuning and management

When to Choose Logfire

  • Ready-to-use experience: You want observability that works immediately
  • AI/LLM applications: You need purpose-built AI observability features
  • SQL familiarity: You prefer one familiar query language
  • Minimal ops burden: You don't want to maintain observability infrastructure
  • Live debugging: You want real-time visibility that actually works

When to Choose Grafana

  • Existing investment: You're already running the Grafana stack
  • Custom dashboards: You need highly customized visualizations
  • Multi-source aggregation: You want to combine data from many different sources
  • Full control: You want to own and customize every aspect

Key Differences Explained

Ready-to-Use vs Build-It-Yourself

Here's what a Grafana user told us:

"The Grafana Tempo and Prometheus, they are just like collectors. They are not doing anything for you. Then you need to set up everything for yourself... no matter how hard we tried and we actually did pour significant developer time into that, I still cannot have reasonable dashboards similar to Logfire front end."

Grafana is powerful but requires significant investment:

  • Deploy and configure Tempo, Loki, Prometheus
  • Learn three different query languages (PromQL, LogQL, TraceQL)
  • Build dashboards from scratch
  • Tune performance and retention

Logfire works immediately:

  • Three lines of code to start
  • Purpose-built UI that understands your data
  • No dashboard building required
  • AI-specific panels out of the box

Live View

"What I really love about Logfire is the Live view. Having this reasonably robust in Grafana plus Grafana Tempo... I could not get it work."

Real-time observability is table stakes for debugging. Logfire's Live View shows you what's happening right now with "pending spans": requests in flight, operations in progress.

Getting equivalent functionality in Grafana requires significant configuration and often doesn't work as reliably.

Query Language — Essential for Agentic Coding

Grafana requires learning multiple query languages:

  • PromQL for metrics
  • LogQL for logs
  • TraceQL for traces

Logfire uses SQL with PostgreSQL-compatible syntax for everything. This is a significant advantage for AI-assisted development:

  • One language — No context-switching between query syntaxes
  • AI assistants excel at SQL — GPT-5, Claude, and coding agents write excellent SQL
  • Agentic workflows — When coding agents debug your AI application, they can write arbitrary queries
  • Familiar syntax — No new query language to learn

When you're iterating on AI applications with coding agents, the agent needs to understand production behavior. With SQL, it can ask any question. With multiple proprietary DSLs, it's constrained to what someone anticipated for each query type.

AI/LLM Observability

Grafana has no native AI support. You'd need to:

  • Instrument manually or with OpenLLMetry
  • Build custom dashboards for AI metrics
  • Create your own panels for conversations, tool calls, etc.

Logfire has purpose-built AI features:

  • One function call instruments your AI framework
  • LLM panels that understand conversations and tool calls
  • Token tracking and cost monitoring built in

Migration Path

If you're running Grafana and want to try Logfire:

  1. Run both: Logfire can run alongside your existing Grafana setup
  2. Compare experience: Try the same debugging task in both
  3. Gradual transition: Start with new projects or AI workloads

Since both support OpenTelemetry, you can even send the same traces to both during evaluation.

Summary

Choose Logfire for observability that works out of the box with purpose-built AI support.

Choose Grafana if you're invested in the ecosystem and willing to build and maintain custom dashboards.