Comparison

Logfire vs Grafana

Grafana is powerful, but you wire it together yourself — Tempo, Loki, Prometheus, and every dashboard. Logfire gives you the same insights out of the box, with one query language and zero infrastructure to manage.

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Feature Comparison

Quick comparison

Feature	Logfire	Grafana
Platform	Managed SaaS (no infra to manage)	Grafana Cloud (managed) or self-hosted Tempo, Loki, Prometheus
Setup	One line: logfire.configure()	Connect each backend, build dashboards
Configuration	Minimal, works immediately	Extensive dashboard building required
Query Language	SQL (Postgres-compatible) for everything	PromQL, LogQL, TraceQL (one per signal)
AI/LLM Support	First-class, purpose-built panels	Manual dashboards; no purpose-built LLM panels
Live View	Built-in real-time view	Complex to configure reliably
Maintenance	Zero (we handle it)	Ongoing dashboard & query upkeep

Key Differences

Why teams choose Logfire

Ready-to-use vs build-it-yourself

Grafana is powerful, but it's a toolkit you assemble yourself: whether you self-host Tempo, Loki, and Prometheus or run them on Grafana Cloud, you still wire up each signal, learn three query languages, build dashboards from scratch, then tune them over time. Logfire works immediately — three lines of code, a purpose-built UI that understands your data, no dashboard building, and AI-specific panels out of the box.

One query language, built for agentic coding

Grafana needs PromQL for metrics, LogQL for logs, and TraceQL for traces. Logfire uses SQL with PostgreSQL-compatible syntax for everything — no context-switching, and coding agents write excellent SQL. When an agent debugs your AI application it can ask any question of production, instead of being constrained to what someone anticipated in each proprietary DSL.

Live View that actually works

Real-time observability is table stakes for debugging. Logfire's Live View shows what's happening right now with pending spans — requests in flight and operations in progress. Getting equivalent functionality in Grafana plus Tempo takes significant configuration and often doesn't work reliably.

First-class AI support

Out of the box, Grafana has no purpose-built AI/LLM observability: you'd instrument manually or with OpenLLMetry and build custom dashboards for AI metrics. Logfire has purpose-built AI features — one function call instruments your AI framework, LLM panels understand conversations and tool calls, and token and cost tracking is built in.

Decision Guide

Which should you choose?

Choose Logfire if...

✓You want observability that works immediately, not a build-it-yourself project
✓You're building AI applications and need purpose-built AI observability
✓You prefer one familiar query language (SQL) across traces, logs, and metrics
✓You don't want to maintain observability infrastructure
✓You want real-time Live View debugging that actually works

Choose Grafana if...

•You're already invested in and running the Grafana stack
•You need highly customized, hand-built dashboards
•You want to aggregate data from many different sources
•You want to own and customize every aspect of the pipeline

FAQ

Common questions

How is setup different from the Grafana stack?

Logfire is one line — logfire.configure() — sending data to a purpose-built UI that already understands it. With Grafana you assemble the stack: whether self-hosted or on Grafana Cloud, you wire up Tempo, Loki, and Prometheus, build dashboards from scratch, and tune performance and retention yourself.

Do I have to learn PromQL, LogQL, and TraceQL?

Not with Logfire. Grafana uses a different query language per signal — PromQL for metrics, LogQL for logs, TraceQL for traces. Logfire uses SQL (PostgreSQL-compatible) for everything. That's a real advantage for agentic coding: coding agents write excellent SQL, so an agent debugging your app can ask any question of production instead of being constrained to a proprietary DSL.

Does Logfire have a real-time Live View?

Yes, built in. Logfire's Live View shows what's happening right now with pending spans — requests in flight and operations in progress. Getting equivalent functionality in Grafana plus Tempo takes significant configuration and often isn't reliable.

What about AI/LLM observability?

Out of the box, Grafana has no purpose-built AI/LLM observability — you'd instrument manually (or with OpenLLMetry) and build custom panels for conversations, tool calls, and cost. Logfire is AI-native: one function call instruments your AI framework, with LLM panels that understand conversations and tool calls plus automatic token and cost tracking.

Can I run Logfire alongside Grafana?

Yes. Both support OpenTelemetry, so you can send the same traces to Logfire and your existing Grafana setup during evaluation, compare the same debugging task in both, and transition gradually — starting with new projects or AI workloads.

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