MLflow vs Weights & Biases: experiment tracking in 2025

Experiment tracking is non-negotiable. Picking the wrong tool means either vendor lock-in or maintenance burden. Here’s the honest comparison.

Quick setup comparison

MLflow (self-hosted)

import mlflow

mlflow.set_tracking_uri("http://your-server:5000")
mlflow.set_experiment("baseline-comparison")

with mlflow.start_run():
    mlflow.log_param("lr", 0.001)
    mlflow.log_metric("val_accuracy", 0.87)
    mlflow.sklearn.log_model(model, "model")

Weights & Biases

import wandb

wandb.init(project="baseline-comparison", config={"lr": 0.001})
wandb.log({"val_accuracy": 0.87})
wandb.finish()

W&B is fewer lines, richer UI out of the box. MLflow is more control.

Decision framework

Factor	MLflow	W&B
Data residency requirements	Self-host possible	SaaS only (enterprise plan for private)
Team size	Any	Any
LLM/diffusion tracking	Basic	Excellent (Tables, Artifacts)
Model registry	Built-in	Built-in
Monthly cost (10 users)	~$0 (infra only)	~$150

What went wrong

Ran MLflow on a free-tier EC2 t2.micro. Tracking server OOMed after 500 runs. Size your tracking server at minimum 4GB RAM or use RDS for the backend store.

Checklist

• Set MLFLOW_TRACKING_URI in CI/CD environment
• Log artifacts (not just metrics) — models, plots, confusion matrices
• Tag runs with git commit SHA for reproducibility
• Archive stale experiments to avoid UI clutter