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As AI does not exist without consequences but quite materially and as its applications have tangible ecological, social and economic impacts, it is an important aspect of the development of an AI-solution to keep its sustainability in mind. Not only can this include ecological decisions which can have an impact on the CO2 emissions – as for example which model is deployed, where, how and when it is trained, and for which use cases it is made – but also questions of social and economic sustainability, which might tackle questions like how does this application impact working conditions. Economic sustainability should not be understood as serving private commercial interests, but as the ability of an AI project to sustain itself. Another question spanning all three fields of ecological, social and economic sustainability is the question of interoperability and the reusability of models and data, which are key factors to sustain small AI projects and to reimplement them for example in other domains as well.