Cohere (language understanding and generation with large language models) is very different from Sagemaker (general ML platform). Cohere abstracts training and

Cohere (language understanding and generation with large language models) is very different from Sagemaker (general ML platform). Cohere abstracts training and deploying language models for developers and companies that don't have an army of MLEs to collect billions of training tokens and figure out TPU/GPU training/serving of massive models. Consider that BERT was published in 2018 and then put into mass production to power Google Search in 2019 [1]. For companies and devs other than big tech, the cost and required knowhow to put these models into production is staggering. Even deploying open source models (which we love) requires overhead in compute and knowhow. Services like Cohere lower the barrier for those who need access to this tech in a managed way. Generation use cases often don't even involve user data beyond an input prompt. In embedding use cases, the user only sends the text they want embedded and get their vectors in return. [1] https://blog.google/products/search/search-language-understanding-bert/ https://blog.google/products/search/search-language-understa... Edit: Cohere engineer here.