MLOps & experiment tracking · Linux Foundation (LF AI & Data); originated at Databricks

MLflow

Open-source experiment tracker and model lifecycle toolkit, governed by the Linux Foundation.

Visit MLflow Alternatives

MLflow is an open-source platform for managing the machine learning lifecycle, originally built at Databricks and now maintained under the Linux Foundation's LF AI & Data umbrella. Its core component, MLflow Tracking, logs parameters, metrics, and artifacts from training runs so they can be compared and reproduced — placing it in the experiment-tracking bucket alongside Weights & Biases, Comet, neptune.ai, and ClearML, rather than production model-monitoring tools. MLflow Projects packages reproducible training code, MLflow Models standardizes packaging across frameworks, and the Model Registry manages versioning and stage transitions. It runs self-hosted, using a local or self-managed tracking server, or as a managed service inside Databricks and other cloud platforms. Being free and framework-agnostic makes it a common default for teams that want experiment tracking without a vendor SaaS commitment.

At a glance

Vendor Linux Foundation (LF AI & Data); originated at Databricks
Pricing model Open source + paid options
Free tier Yes
Deployment Cloud, Self-hosted
Open source Yes (Apache-2.0)
Best for Teams wanting free, self-hostable experiment tracking without committing to a vendor's hosted SaaS.

Pricing

MLflow itself is free and open source; any cost comes from the infrastructure or managed platform (e.g. Databricks) you run it on.

Pricing has not been verified yet — see the vendor's site.

Features

  • Experiment tracking (parameters, metrics, artifacts)
  • Model Registry with staged versioning
  • Reproducible project packaging
  • Model packaging across ML frameworks
  • REST API and CLI for automation
  • Built-in model serving for quick deployment
  • LLM tracing and evaluation
  • Autologging for popular ML frameworks

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

Weights & Biases Hosted experiment tracker and AI evaluation platform for logging training runs, metrics, and model lineage. Comet Hosted experiment-tracking platform that also offers a separate LLM observability product (Opik). neptune.ai Experiment tracker built for high-throughput logging of large-scale and foundation-model training runs. ClearML Open-source experiment tracker that also bundles pipeline orchestration, dataset versioning, and model deployment.

MLflow in the index now

Terms to know

Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. Data lineage A traceable record of where data came from, how it was transformed, and where it is used downstream. Data pipeline The automated sequence of steps that moves data from source systems to a destination, transforming it along the way. MLOps (machine learning operations) The set of practices for deploying, monitoring and maintaining machine learning models reliably in production, extending DevOps to ML systems.

Related guides

How to choose an MLOps & experiment tracking tool "MLOps" covers five different jobs — tracking, monitoring, orchestration, serving, versioning — and most teams need more than one, not the biggest single tool.