Langfuse vs MLflow
A side-by-side comparison of Langfuse and MLflow, two Observability tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
Langfuse
ObservabilityOpen-source LLM observability. Self-hostable, OpenTelemetry-native.
View LangfuseAt a glance
| Attribute | Langfuse | MLflow |
|---|---|---|
| Category | Observability | Observability |
| Pricing (differs) | FREEMIUM | FREE |
| License (differs) | Open core | Open source |
| Deployment (differs) | Hybrid | Self-host |
| Platforms (differs) | API, Web | Web, CLI, API, Linux, macOS, Windows |
| Model support | Model-agnostic | Model-agnostic |
| Vendor (differs) | Langfuse | Linux Foundation |
The honest brief
Langfuse
The MIT-licensed, self-hostable answer to LangSmith — own your observability data, framework-agnostic.
- Own your observability data
- Framework-agnostic, OTel-native
- Tracing + evals + prompt mgmt
- Transparent unit-based pricing
- Self-host infra cost at scale
- Less deep LangChain integration
- Setup heavier than hosted-only
MLflow
The most widely adopted open-source option: one platform spanning tracing, evals, prompt registry, and classic ML.
- Fully open source, no lock-in
- OpenTelemetry-based, framework-agnostic
- Built-in metrics and LLM judges
- Large community + Linux Foundation backing
- Self-host on your own infrastructure
- Self-hosting adds operational overhead
- Broad scope can feel heavy for simple needs
- Managed convenience needs Databricks or DIY
- UI less polished than some SaaS rivals