I'll confess I haven't used Tecton myself, but reading through their documentation it seems that they are much more focused on ETL style data pipelines with the final output being to a feature store. Whereas Sematic is looking at the general end-to-end ML pipelines (e.g. not just dataset transformations/feature extractions, but also model training and evaluation). In case it's helpful, we do have a page that compares Sematic with some other tools in a similar domain to us: https://docs.sematic.dev/sematic-vs#...-mlflow-pipelines