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Logfire vs Alternatives

AI Observability That Actually Works

Logfire is AI observability that outperforms because it sees your entire application.

Here's the problem with AI-only tools: They've tied one hand behind their back. When debugging AI applications, you need to know:

  • What triggered the LLM call?
  • What did the AI access (databases, APIs, tools)?
  • What happened with the response?

AI-only tools can only show you the middle part. They're missing the context that makes debugging possible.

Logfire sees everything. When your AI agent fails, you don't just see "LLM returned error". You see the database timeout that caused it, the API rate limit that preceded it, and the user request that started it all.

This isn't a bonus feature. It's what makes AI debugging actually work.

Built on Standards, Works Everywhere

Logfire is built on OpenTelemetry, the industry standard for observability. This means:

  • Any OTel instrumentation works: If your framework has OpenTelemetry support, it works with Logfire. No special integration needed.
  • Any language works: Python, JavaScript/TypeScript, Rust, Go, Java, .NET. If it speaks OTel, it works.
  • Your instrumentation is portable: You're never locked in. Even if you use the Logfire SDK for convenience, you can configure it to send data to other backends
  • Works with any OTel-compatible tools: Integrate with your existing stack

We provide first-party SDKs for Python, JavaScript/TypeScript, and Rust that offer convenient wrappers, but they're optional. Standard OpenTelemetry libraries work perfectly.

Comparison Guides

AI Observability Tools

Traditional Observability Platforms

When to Choose Logfire

Logfire is a great fit when you:

  • Want one stack, not two - AI + infrastructure observability unified, not an agent framework plus a separate APM tool
  • AI enabled workflows - Use the Logfire MCP server to query your app's telemetry data, analyze distributed traces, and fix errors with AI using Logfire's OTel-native API
  • Have polyglot architectures - Python backend + TypeScript frontend, microservices in different languages
  • Want exceptional Python, TS/JS, Rust support - While also supporting other languages via OTel
  • Need code-first evals - pydantic-evals tests any Python function, not just LLM calls
  • Prefer SQL-based querying - Familiar PostgreSQL syntax over proprietary query languages
  • Value simple, predictable pricing - $2/million spans, scales to billions of spans per month
  • Need enterprise features - [SOC2, HIPAA, self-hosting](https://pydantic.dev/pricing#enterprise?utm_source=comparison_docs options