ML Experiment Tracking in 2026: Weights and Biases vs MLflow vs Custom Solutions

Running a machine learning experiment without tracking is like coding without version control. You run 50 experiments with different hyperparameters, and two weeks later you can’t remember which combination produced the best results. Experiment tracking tools solve this, but choosing the right one involves real tradeoffs.

Weights and Biases: Best UX, Highest Cost

Weights and Biases (wandb) has the best developer experience by a wide margin. A few lines of code in your training script, and every metric, hyperparameter, and artifact is tracked in a beautiful web dashboard. The system handles distributed training, hyperparameter sweeps, and model registry without additional configuration.

The cost is the main drawback. wandb is free for personal projects and academic use, but team and enterprise plans become expensive as your team grows. For a 20-person ML team, wandb can cost more than compute. The data lives in wandb’s cloud, which means your experiment history is tied to a third-party service.

MLflow: Open Source, Self-Hosted

MLflow is the open-source alternative. It tracks experiments, manages models, and serves models for inference. You host it yourself, which means lower cost and data ownership, but also operational overhead.

The tracking server runs as a Python process with a file-based or database-backed store. The UI is functional but less polished than wandb. MLflow’s real strength is the model registry — it handles model versioning, staging, and deployment lifecycle management better than any other open-source tool.

The Custom Approach

Some teams build their own tracking with a database, a dashboard, and a few Python utilities. This makes sense when you have specific requirements that off-the-shelf tools don’t meet, or when compliance requires data to stay in specific infrastructure.

The custom approach always costs more than anticipated. Building the tracking is easy. Building the UI that your team actually uses is hard. Maintaining the system as requirements evolve is harder. Most teams that go this route underestimate the ongoing maintenance burden.

The Recommendation for 2026

For teams just starting with ML: start with wandb’s free tier. The developer experience accelerates iteration, and you can export your data if you outgrow it.

For teams running dozens of experiments weekly: MLflow self-hosted is the pragmatic choice. The operational overhead of running a tracking server is lower than wandb’s cost at scale.

For enterprise ML platforms: evaluate both MLflow and the commercial offerings (wandb, Neptune, Comet) against your requirements for data residency, SSO, and compliance. The tooling has converged on a common feature set — experiment tracking, model registry, and artifact management — and the differentiators are UX and pricing.