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.
| 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 | Personal: 10M records free; Team: $49/month + $2/M over 10M | $50 + span and payload usage (AX Pro) |
| Data retention | 30 days default; 90 days (Growth); custom on Enterprise | 30 days on AX Pro; custom on Enterprise |
| Query interface | Standard SQL (PostgreSQL) | Proprietary DSL (Python expression syntax) |
| Human annotations | Annotations on production runs | Annotations on production runs |
| MCP server | Query production traces from your editor or AI agent | Instrumentation guidance and docs; production trace querying not available |
| Workload | Arize AX | Logfire | Difference |
|---|---|---|---|
| 1 user, 5M spans/mo | ~$4,055 | $0 (Personal) | ~$4,055/mo |
| 5 users, 50M spans/mo | ~$40,730 | ~$129 | ~316x |
| 20 users, 500M spans/mo | ~$407,480 | ~$1,229 | ~332x |
Calculation scope
Arize AX Pro span and payload pricing compared with the cheapest applicable public Logfire plan for each listed user and record volume.
Basis: USD; public monthly list price. Prices checked .
The Arize payload model uses 5M × 5 KB = 25 GB per month.
5M records are within the 10M Personal allowance $0 $50 $50 (5M − 50K included) × $0.0008 $3,960 max(25 GB − 10 GB included, 0) × $3/GB $45 The Arize payload model uses 50M × 5 KB = 250 GB per month.
$49 $49 (50M − 10M included) ÷ 1M × $2 $80 $50 $50 (50M − 50K included) × $0.0008 $39,960 max(250 GB − 10 GB included, 0) × $3/GB $720 The Arize payload model uses 500M × 5 KB = 2,500 GB per month.
$249 $249 (500M − 10M included) ÷ 1M × $2 $980 $50 $50 (500M − 50K included) × $0.0008 $399,960 max(2,500 GB − 10 GB included, 0) × $3/GB $7,470 “Covers 10 million logs, spans, and metrics every month.”
The official calculator shows 20 million records and no extra seats totaling $69/month on Team.
“$.0008 per additional span”
AX Pro lists 50,000 spans and 10 GB included, then $0.0008 per additional span and $3 per additional GB.
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.
Logfire's Team plan starts at $49 a month and includes 10 million records; additional records cost $2 per million, with 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.
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.
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.
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.
Dynatrace agreed to acquire Arize in August 2026, aiming to combine Arize's AI evaluation and observability capabilities with Dynatrace's application and infrastructure tracing. The deal is expected to close later in 2026. Pydantic Logfire combines full-stack and AI observability in one product from the start, and hasn't been acquired or merged into another platform. The feature and pricing comparisons on this page reflect Arize AX as it stands today, independent of Dynatrace.
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.
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.
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.
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.
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