Comet is a very powerful tool for experiment tracking and MLOps workflows, but the platform is somewhat complex for teams that are not initially familiar with the structured practices that have to be followed in MLOps. Understanding experiment organization, integrations, and tracking workflows requires some onboarding. Pricing is one of the major challenges that Comet is facing. As our organization has increased and many users and experiment tracking requirements have increased, the platform cost can increase very quickly. The platform delivers very strong value when the users have increased or experiment tracking has increased extensively. However, as the ML workload increases, the cost also increases very quickly. Smaller teams running a limited number of ML experiments may not be able to fully utilize its capabilities as a whole. Comet has good integration capabilities with popular ML frameworks, and the integration is very strong. While using some customized pipelines, we need to have some manual configuration, and some effort is needed in that area. The slight learning curve for teams that are unfamiliar with structured MLOps practices could have some improvement in that area. Some integrations with customized pipelines still require a lot of manual effort, which is one area that Comet could improve in. Pricing initially seemed very high compared to other open-source experiment tracking tools. However, once we integrated the platform into our workflows, the productivity improvements justified the investment.
