Machine Learning Experiment Tracking Tools: MLflow, W&B and ClearML

An experiment tracking system is the system of record for what happened during training: runs, parameters, metrics, artefacts and model lineage. It owns the metadata that makes a model reproducible and the registry that makes promotion controlled.

It does not own pipeline scheduling, compute execution or serving. The boundary matters: when a tracking tool also tries to orchestrate steps, the failure boundary blurs and an outage in tracking cascades into training. The three tools here draw that boundary differently.

To compare fairly, the same tracking workflow is run in each tool: start a run, log parameters and metrics, save an artefact, register a model version, stage and promote it through an alias, then export the run record. The workflow is the unit of comparison, not individual features.

Where a tool lacks a step natively — for example a formal registry — the workflow is approximated with the closest equivalent, and the limitation is recorded. This keeps the comparison honest about what each tool can and cannot own.

All three tools represent a run as a unit of execution with parameters, metrics and artefacts. MLflow stores runs in a backend store with artefacts in a configurable object store. W&B stores runs in a managed backend with rich metric history and dashboarding. ClearML stores runs in a self-hosted server with artefacts in object storage.

The differences are in query and comparison: W&B's dashboarding is the most collaborative, MLflow's data model is the most portable through its REST API, and ClearML ties runs tightly to its task execution model.

MLflow has a formal model registry with versions, stages and aliases that serving can consume directly through an alias. This is the most explicit promotion boundary of the three. ClearML also has a model registry with versions and publication state.

W&B's registry is artefact-based: models are artefacts with aliases, and promotion is handled through artefact collections and aliases. It works, but the lifecycle is less formal than MLflow's dedicated registry, which matters for teams that need audit trails on promotion.

Lineage connects a model version to the dataset and run that produced it. MLflow records run-to-artefact lineage through the registry; dataset identity is recorded as a run parameter unless a separate data versioning tool is used. W&B artefacts provide explicit dataset-to-model lineage through artefact dependencies.

ClearML links datasets to tasks and models through its dataset management feature. None of the three is a full data versioning system; all three integrate with DVC or object-store versioning for the dataset source of truth.

MLflow and ClearML can be fully self-hosted with open-source licenses, which suits air-gapped and data-controlled environments. W&B's primary offering is managed SaaS, with a self-hosted enterprise edition available for organisations that need it.

Self-hosting shifts backup, upgrade and scaling to the operator. Managed offerings shift those to the vendor at the cost of a data model and a recurring fee. The deployment model is the first decision because it determines who owns incidents.

MLflow's open-source server has limited built-in authentication and RBAC; full RBAC is part of the managed Databricks offering or third-party proxies. W&B provides team workspaces, sharing and enterprise RBAC as a first-class feature. ClearML's open-source server has basic auth with RBAC extended in the enterprise edition.

Collaboration depth follows the same pattern: W&B is the most dashboard- and collaboration-oriented, MLflow is the most minimal, and ClearML sits between them. Choose the model that matches how your team actually shares experiments.

All three integrate with PyTorch, TensorFlow, scikit-learn, XGBoost and common CI systems through SDKs and autologging. MLflow's autologging is the broadest in the open-source ecosystem. W&B's SDK integrates deeply with its dashboarding. ClearML's SDK integrates with its execution and orchestration hooks.

Integration friction is low for all three on standard frameworks. The differentiator is CI/CD: MLflow's registry aliases integrate cleanly with deployment pipelines, and W&B's artefact triggers integrate with its automation features.

MLflow and ClearML can run fully air-gapped because their servers are self-hosted and open-source. W&B's managed SaaS requires outbound connectivity; the self-hosted enterprise edition can operate in restricted networks but is not the default offering.

For regulated environments, the question is whether the tracking server can run without any outbound internet. MLflow and ClearML answer yes by default; W&B answers yes only with the enterprise self-hosted edition.

For self-hosted MLflow and ClearML, the operator backs up the backend database and artefact store and tests restore. For W&B managed SaaS, the vendor handles platform backup; for the self-hosted enterprise edition, the operator handles it.

Scaling responsibility follows the same split. Self-hosted means you scale the backend, storage and (for ClearML) the execution agents. Managed means the vendor scales within your plan's limits, which is where usage-based cost grows.

This comparison proves which tool owns which tracking responsibility and how they differ on deployment and collaboration. It does not prove that any tool makes models more accurate, that a managed platform is cheaper at scale, or that one tool is the overall winner.

Tracking records what happened; it does not judge whether the model is correct. A run with better metrics can still be overfit. The comparison is about ownership boundaries and operational fit, not model quality.