Arize Phoenix vs MLflow
Both are observability & tracing tools. Here is how they actually differ on price, billing model and deployment.
Arize Phoenix
The default open-source choice for LLM tracing and eval - runs locally in under a minute, built on OpenTelemetry. The catch is the license - the server is Elastic License 2.0, source-available, not OSI open source.
MLflow
The open-source ML platform that grew a serious GenAI half. MLflow 3 adds OpenTelemetry-compatible tracing, LLM judges and review apps - free and self-hostable, with Databricks selling the managed version. The best zero-cost option if you can run infrastructure.
| Arize Phoenix | MLflow | |
|---|---|---|
| Category | Observability & Tracing | Observability & Tracing |
| Our rating | 4/5 | 4/5 |
| Starting price | $0 | $0 (open source) |
| Billing meter | No usage metering | No usage metering |
| Free plan | Yes | Yes |
| Free self-hosting | Yes, free | Yes, free |
| Best for | Teams that want fast, framework-agnostic OSS tracing and strong RAG eval, and who are not reselling Phoenix as a hosted service | Teams that already run MLflow for classical ML, Databricks customers, and anyone who wants a genuinely free and complete self-hosted platform and has the operational capacity to run it. |
Our verdict on Arize Phoenix
The default open-source pick for good reasons - it starts in under a minute, the RAG eval is the best around, and it is built on OpenTelemetry so you are not locked to one framework. Just read the license before you build a business on it. The server is Elastic License 2.0, source-available rather than OSI open source, which only bites if you plan to offer Phoenix as a service - but it is not what "fully open source" implies.
Full Arize Phoenix review →Our verdict on MLflow
MLflow is the strongest zero-cost option in the category, with the caveat that free software is not free to operate. MLflow 3 turned what was an experiment-tracking tool into a real GenAI platform - OpenTelemetry-compatible tracing from a single line of code, built-in and custom LLM judges, review apps that collect expert feedback and align automated judges against it, and evaluation datasets built directly from production traces. It is Apache 2.0 and the open-source build is complete rather than a gated teaser, which is more than can be said for several commercial competitors advertising self-hosting. Two honest caveats. The UI is functional rather than pleasant, and it shows its lineage as a tool built for ML engineers rather than application developers. And the genuinely best-governed experience - Unity Catalog trace storage in OTel Delta tables, no storage cap, SQL queryable - is available on Databricks, which is where the commercial gravity sits. If you already run MLflow or Databricks, this is close to automatic.
Full MLflow review →Frequently Asked Questions
What is the main difference between Arize Phoenix and MLflow?
Arize Phoenix: Teams that want fast, framework-agnostic OSS tracing and strong RAG eval, and who are not reselling Phoenix as a hosted service MLflow: Teams that already run MLflow for classical ML, Databricks customers, and anyone who wants a genuinely free and complete self-hosted platform and has the operational capacity to run it. Both sit in Observability & Tracing, so the decision usually comes down to billing model and deployment rather than raw capability.
Which is cheaper, Arize Phoenix or MLflow?
It depends entirely on your workload shape, because they meter differently - Arize Phoenix bills on no usage metering and MLflow bills on no usage metering. Published starting prices are $0 and $0 (open source) respectively, but those numbers are not comparable until you apply them to the same traffic. Use our cost calculator to model both against your own request volume and span count.
Can I self-host Arize Phoenix or MLflow?
Arize Phoenix: Yes, free. MLflow: Yes, free. Free self-hosting means no licence fee, not no cost - you still own the infrastructure, upgrades and on-call.