--- title: 'Logfire vs Arize AX: Developer-First AI Observability' description: Compare Logfire and Arize AX. See why teams choose Logfire for full-stack application observability with excellent AI support. canonical: https://pydantic.dev/logfire/vs-arize last-reviewed: 2026-07-13 --- > Markdown version of [Logfire vs Arize AX](https://pydantic.dev/logfire/vs-arize) — the canonical HTML page. > > Site index: [/llms.txt](https://pydantic.dev/llms.txt) --- # Logfire vs Arize AX Both provide AI observability but differ in scope. Arize AX monitors your AI layer, while Logfire covers your entire application in one trace: LLM calls, database queries, APIs, and frontend. When agents fail in production, you see the complete picture, not just what happened inside the model. 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. [Try Logfire free](https://logfire.pydantic.dev/) ## Quick comparison | Feature | Logfire | Arize AX | | ------------------- | ------------------------------------------------------- | -------------------------------------------------------------------------- | | Observability scope | Full-stack (AI + databases, APIs, infra) | AI/LLM tracing + ML model monitoring | | Strength | Application tracing + AI | Drift detection, model performance, agent evaluation | | Non-AI tracing | Full support | OTel-compatible but no first-class support for web frameworks or databases | | Language support | Python, JS/TS, Rust + any OTel language | Python-focused | | Pricing | $2/million spans + $25/seat (if more than 5 seats) | $10/million spans + $3/GB payload (AX Pro) | | Data retention | 30 days default; 90 days (Growth); custom on Enterprise | 15 days on AX Pro; custom on Enterprise | | Query interface | Standard SQL (PostgreSQL) | Proprietary DSL (Python expression syntax) | | Human annotations | Not available | Built in | | MCP server | Query production traces from your editor or AI agent | Instrumentation guidance and docs; production trace querying not available | ## Pricing comparison | Workload | Arize AX | Logfire | Difference | | ----------------------- | -------- | -------------- | ---------- | | 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. ## 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 behavior directly, something no proprietary query language makes easy. ## 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 **What's the difference between Logfire and Arize AX?** Arize AX comes from the ML monitoring world, excelling at model drift detection, performance metrics, and agent evaluation. Logfire comes from the application observability world, excelling at distributed tracing across your entire stack, databases, APIs, infrastructure monitoring, and real-time debugging, alongside AI. The biggest practical difference is scope. Logfire sees your whole application. Arize AX focuses on the model and AI layer. **Is Arize Phoenix open source?** Yes. Arize Phoenix is open source and self-hostable with no feature gates. This comparison page is against Arize AX, Arize's commercial SaaS platform, which is what most teams reach for when they need a production-ready hosted solution with managed retention, alerting, and enterprise features. If you're evaluating Phoenix OSS specifically, the comparison looks different. Phoenix is free and self-hosted, but you manage your own infrastructure, retention, and scaling, which can become expensive. **How does developer experience compare?** With Logfire, three lines gets you started: `import logfire`, `logfire.configure()`, `logfire.instrument_openai()`. From there, instrumenting FastAPI, SQLAlchemy, HTTPX, and most common libraries is one more line each. Arize AX is optimised for ML engineers working in the model layer. For general application tracing (web frameworks, databases, background workers) there are no first-class integrations, so you're working with raw OTel setup. **Does Logfire support drift detection?** Logfire focuses on application observability and real-time AI tracing. For specialised ML model monitoring features like drift detection and model performance metrics, Arize AX is better suited. You can use both tools together. **Can I use both Logfire and Arize AX?** Yes. Use Logfire for full-stack application observability, real-time debugging, and querying your traces via SQL or MCP. Use Arize AX for specialised ML monitoring like drift detection and model performance metrics. Both are built on OpenTelemetry, so they integrate cleanly. **Does Logfire support distributed tracing?** Yes. Logfire's OpenTelemetry implementation stitches distributed spans into traces automatically, with no special configuration needed. Spans from different services connect into a single trace out of the box. ## Ready to switch from Arize AX? Get started with 10 million free spans per month. No credit card required. Start free: https://logfire.pydantic.dev/ — [View pricing](https://pydantic.dev/pricing) --- See also: [Logfire alternatives](https://pydantic.dev/logfire/alternatives) ([markdown](https://pydantic.dev/logfire/alternatives.md)), [Pydantic Logfire](https://pydantic.dev/logfire) ([markdown](https://pydantic.dev/logfire.md)), and [pricing](https://pydantic.dev/pricing.md).