MLflow review and pricing

MLflow is the open-source MLOps baseline, originally released by Databricks in June 2018 and now the most widely deployed open-source ML lifecycle project. The project covers four core surfaces: experiment tracking (the most-used component), model registry, project packaging, and model serving. MLflow is the de facto integration point for almost every commercial MLOps platform: Vertex AI, SageMaker, Azure Machine Learning, Databricks Mosaic AI, Weights and Biases, Comet, and Neptune.ai all integrate with MLflow either as a tracking backend, a model-registry import path, or a model-serving format. Strengths: free and open source under Apache 2.0, hostable on your own infrastructure, deep integration into the broader ML ecosystem, stewarded by Databricks (the company that pays the core maintainers), and the honest baseline for teams unwilling to pay for proprietary tracking. Trade-offs: self-hosting requires real ops investment, the UI is functional rather than slick, model registry governance is thinner than Vertex AI or SageMaker, MLflow as a hosted SaaS only exists inside Databricks (no neutral cloud-hosted MLflow with full enterprise features), and contribution velocity outside Databricks has slowed since 2022 as Databricks centralized stewardship.