⚡ TL;DR — 30-Second Verdict
Choose MLflow if you're in the model training and experimentation phase — it's the standard for tracking training runs, comparing models, and managing the ML lifecycle. Choose Langfuse if you're building LLM applications and need to trace prompt chains, evaluate outputs, and monitor costs in production. Use both if you train models with MLflow and then serve them via LLM apps monitored with Langfuse.
Quick Comparison
| Feature | MLflow | LangFuse |
|---|---|---|
| Primary focus | ML training tracking + model registry | LLM tracing + prompt evaluation |
| LLM tracing | Basic LLM tracking (newer) | First-class LLM tracing |
| Prompt management | Limited | Full prompt versioning + A/B |
| Training metrics | Full experiment tracking | Not designed for training |
| Model registry | Full model lifecycle | No model registry |
| Cost tracking | No LLM cost tracking | Token + cost per trace |
| Self-hosting | Easy self-host | Cloud or self-host |
What Is MLflow?
MLflow has found solid traction with 18k+ GitHub stars, indicating real-world adoption beyond early adopters. A well-regarded open-source tool with a strong community and active development. The feature set covers the main use cases, though some advanced workflows require configuration beyond the defaults.
— AI Nav Editorial Team on MLflow
What Is LangFuse?
A specialized tool, LangFuse targets a specific need rather than trying to cover every use case. Best used when you need to run models locally without sending data to external services. The installation requires more technical knowledge than Ollama, but gives you lower-level control over quantization and serving configuration.
— AI Nav Editorial Team on LangFuse
→ Read the full LangFuse review