
Langfuse
8.2#1 in prompting toolFrom $29 a monthFree planWebAPILinuxSelf-hosted
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| Fact | Langfuse | MLflow Tracing |
|---|---|---|
| Documentation | Documentation covers observability, prompt management, evaluation, metrics, APIs, SDKs, integrations, compatibility, deployment, and security with versioned self-hosting guides. | MLflow documentation has separate sections for LLM and agent development, tracing, and evaluation, and for traditional machine-learning workflows. |
| Evaluation | Evaluation features include LLM-as-a-judge, code evaluators, user feedback, manual labeling, datasets, and experiments. | MLflow connects trace inspection with evaluation results to help assess application quality. |
| Integrations | Langfuse lists more than 100 integrations spanning OpenTelemetry, Python and TypeScript, model providers, agent frameworks, gateways, and developer tools. | The integrations directory organizes automatic tracing support across frameworks, coding agents, and model providers. |
| Licence | Langfuse states that all product features are MIT licensed, while a separate Enterprise license covers governance features such as project RBAC, audit logs, SCIM, and retention policies. | The public MLflow repository identifies its licence as Apache-2.0. |
| Rating on G2 | 4.5 out of 5 from 1 review | No product rating or review count for MLflow Tracing is stated on the opened G2 MLflow marketplace page. |
| Rating on TrustRadius | No rating or review count displayed on the Langfuse profile | 8 out of 10 from 3 reviews |
| Self-hosting | The OSS edition can be deployed with Docker Compose, Kubernetes Helm, or Terraform guides for AWS, GCP, and Azure, including offline or air-gapped installations. | The repository's setup instructions show starting an MLflow server locally and connecting tracing to it. |
Langfuse suits teams building AI agent applications that need tracing, prompt management, and evaluation tools. It also supports teams that want to self-host the core on their own infrastructure.
MLflow Tracing suits teams building LLM applications or agents that need visibility into request steps, tool calls, latency or token costs. It supports both automatic integrations and manual instrumentation, with trace data hosted on their infrastructure.