MLflow, an open-source platform for experiment tracking and machine learning operations, is free to download but incurs operational costs when self-hosted, primarily due to the infrastructure and maintenance demands. Deploying MLflow, particularly on AWS, involves expenses for the tracking server, metadata store, and artifact store, with a typical setup costing around $200 per month, not including additional storage and data transfer fees. While AWS offers various deployment options such as EC2 instances, ECS with Fargate, and Kubernetes, each comes with its own cost and complexity considerations. Maintenance responsibilities include regular software updates, monitoring resource utilization, and ensuring security and compliance, which can be labor-intensive without a dedicated DevOps team. Despite being a cost-effective and flexible solution for many data science teams, organizations must weigh the operational burden against the potential benefits and consider whether a managed SaaS platform might offer a more economical and efficient alternative.