MLflow vs LangSmith
Both are observability & tracing tools. Here is how they actually differ on price, billing model and deployment.
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.
LangSmith
LangChain's proprietary observability and eval platform. Turnkey and deeply integrated with LangChain and LangGraph - but closed-source, and the trace bill explodes at production scale.
| MLflow | LangSmith | |
|---|---|---|
| Category | Observability & Tracing | Observability & Tracing |
| Our rating | 4/5 | 3/5 |
| Starting price | $0 (open source) | $39/seat/mo |
| Billing meter | No usage metering | seat |
| Free plan | Yes | Yes |
| Free self-hosting | Yes, free | No or paid tier only |
| Best for | 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. | Teams already all-in on LangChain and LangGraph who want the tightest-integrated observability and don't mind the bill |
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 →Our verdict on LangSmith
The most turnkey observability platform if you already live in LangChain and LangGraph - the tracing is zero-config and the eval tooling is genuinely good. But it's closed source, self-hosting is Enterprise-only, and the trace bill is roughly 25x Langfuse at scale. It's great until you scale or want out, and you can't self-host your way around either problem.
Full LangSmith review →Frequently Asked Questions
What is the main difference between MLflow and LangSmith?
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. LangSmith: Teams already all-in on LangChain and LangGraph who want the tightest-integrated observability and don't mind the bill Both sit in Observability & Tracing, so the decision usually comes down to billing model and deployment rather than raw capability.
Which is cheaper, MLflow or LangSmith?
It depends entirely on your workload shape, because they meter differently - MLflow bills on no usage metering and LangSmith bills on seat. Published starting prices are $0 (open source) and $39/seat/mo 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 MLflow or LangSmith?
MLflow: Yes, free. LangSmith: No or paid tier only. Free self-hosting means no licence fee, not no cost - you still own the infrastructure, upgrades and on-call.