Comparison

Logfire vs Arize AX

Arize offers two products: Phoenix, an open source tool for LLM tracing and evaluation, and Arize AX, a commercial SaaS platform for ML and AI monitoring. This page compares Logfire against Arize AX.

Feature Comparison

Quick comparison

FeatureLogfireArize AX
Observability scopeFull-stack (AI + databases, APIs, infra)AI/LLM tracing + ML model monitoring
StrengthApplication tracing + AIdrift detection, model performance, agent evaluation
Non-AI tracingFull supportOTel-compatible but no first-class support for web frameworks or databases
Language supportPython, JS/TS, Rust + any OTelPython-focused
Pricing$2/million spans + $25/seat (if more than 5 seats)$10/million spans + $3/GB payload (AX Pro)
Data retention30 days default; 90 days (Growth); custom on Enterprise15 days on AX Pro; custom on Enterprise
Query interfaceStandard SQL (PostgreSQL)Proprietary DSL (Python expression syntax)
Language supportPython, JS/TS, Rust + any OTel languagePython-focused
Human annotationsNot availableBuilt in
MCP serverYesNot available

Cost Savings

Pricing comparison

WorkloadArize AXLogfireDifference
1 user, 5M spans/mo~$99$0 (free tier)~$99/mo
5 users, 50M spans/mo~$999~$129~8x
20 users, 500M spans/mo~$12,249~$1,229~10x

*Arize Phoenix Cloud (AX) Pro pricing ($10/million spans + $3/GB payload). Logfire Cloud Team or Growth plans ($2/million spans). Arize's dual-axis billing particularly impacts RAG architectures where retrieval context adds significant payload size per trace.

Key Differences

Why teams choose Logfire

One trace, complete context

When something breaks in production, you need to know whether it was the model, the data, or the infrastructure. Arize AX shows you the AI layer. Logfire shows you the whole thing: the user request, the database query, the LLM call, the API response. One trace, complete context. Arize AX can receive OTel spans from non-AI services, but has no first-class support for web frameworks or databases.

Simpler pricing, better retention

Logfire charges $2 per million spans, flat. No payload surcharge. You can afford to ingest everything, which means when something fails in production, the trace is complete and easier to debug and recover.

Standard SQL, no proprietary query language

Logfire uses standard (PostgreSQL) SQL throughout. Your team already knows it. AI coding assistants write excellent SQL, which means you can ask questions about your production traces without leaving your editor. Logfire also ships an MCP server. Point your coding agent at your Logfire data and it can answer arbitrary questions about production production behavior directly, something no proprietary query language makes easy.

Decision Guide

Which should you choose?

Choose Logfire if...

  • You need to trace your entire app, not only AI calls
  • You have services in multiple languages
  • You want the simplest possible setup (3 lines of code)
  • You prefer one tool for both AI and application monitoring
  • You want to query traces with SQL or via an MCP server

Choose Arize AX if...

  • Your team thinks in ML metrics, not application traces
  • You want human-in-the-loop annotations built-in
  • You're already invested in Arize's broader ML platform

FAQ

Common questions

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