that sounds obvious, but think about what it means. every model tracking tool, every experiment logger, every serving platform assumes you have dedicated infrastructure people. that you know what mlflow is and have opinions about it. that you've read papers on model drift.
most teams building with ml don't have that. they have a model that works. they want to ship it. they want to know if it breaks.
the PhD tax right now, if you want to do ml "the right way," you pay a tax. you either:
hire someone who knows the ecosystem spend weeks learning tools built for different problems skip observability entirely and hope for the best option 3 is what most people choose. not because they don't care, but because the alternatives cost too much.
we think that's wrong.
what we're building aither is ml observability for the rest of us.
track your models. understand their predictions. serve them. log everything. no phd required.
the goal is stripe-level simplicity. you shouldn't need to understand our architecture. you should be able to add three lines of code and see what your model is doing.