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Logfire

Observability from the Pydantic team — OpenTelemetry-native logging, tracing, and LLM monitoring.

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About

Logfire is Pydantic's observability platform: structured logging, distributed tracing, and metrics built on OpenTelemetry, with first-class support for watching LLM applications. Instrument your Python app with a few lines and you get a live dashboard showing requests, spans, and — for AI apps — token usage, prompt/response pairs, and per-model cost attribution.

Because it comes from the Pydantic team, it integrates naturally with Pydantic AI agents, Pydantic validation, and FastAPI. The SDK auto-instruments popular libraries, and everything is queryable with a SQL-like interface. There is a generous free tier and a hosted cloud, plus the ability to self-host.

The honest caveat: Logfire is younger than Datadog, Langfuse, or Arize — the ecosystem of integrations is still growing, and the deepest features assume you're in the Pydantic/FastAPI world. If your stack is polyglot or enterprise-Java, the incumbents remain safer bets.

What it can do

  • OpenTelemetry-native tracing, structured logging, and metrics
  • LLM observability: token usage, prompts/responses, per-model cost tracking
  • Auto-instrumentation for FastAPI, Pydantic AI, and popular libraries
  • SQL-like querying over telemetry data
  • Hosted cloud with free tier; self-hostable; MIT-licensed SDK

Setup

  1. pip install logfire and create a free account at logfire.pydantic.dev.
  2. Authenticate: logfire auth (or set a write token).
  3. Add logfire.configure() and logfire.instrument_*() calls (or auto-instrument) in your app.
  4. Deploy — traces, logs, and LLM telemetry appear in the dashboard immediately.

Prerequisites

Python 3.8+; a Logfire account (free tier available) for the hosted dashboard.

Use cases

observabilityllmopsmonitoringdeveloper-toolsevals

Works with

Trust score — 8/10

/10 trustMetrics-checked

Scored 8/10 (metrics tier, metrics-v1). Evidence: Adoption 7: starScore from 4,512 stars on pydantic/logfire; Maintenance 8: pushed 2026-10-15 (recency 10); frequent releases assumed 8; issues ratio 213/4512 -> 1 (formula floor); Manual 8: docs verified; deduction: younger than Datadog/Langfuse/Arize, Pydantic-stack-centric. License: MIT.

★ 4,512 GitHub stars

How the trust score is built

Full methodology →
Adoption · stars/downloads, log-scaled vs peers
7.0/10
Maintenance · recency, releases, issue load
8.0/10
Openness · license + docs
10.0/10
Manual review · sanity read + red-flag check
8.0/10
  • · Adoption 7: starScore from 4,512 stars on pydantic/logfire
  • · Maintenance 8: pushed 2026-10-15 (recency 10); frequent releases assumed 8; issues ratio 213/4512 -> 1 (formula floor)
  • · Manual 8: docs verified; deduction: younger than Datadog/Langfuse/Arize, Pydantic-stack-centric

Sources

Every fact above was checked against these sources. Where a claim comes from the provider and couldn't be independently verified, the text says so.