Cog is optimized for getting a deep learning model inside a Docker image. We found that ML researchers struggled to use Docker, so we made that process easier. It generates a best practice Dockerfile with all your dependencies, and resolves the CUDA versions automatically. It also includes a queue worker, which we found was the optimal way of deploying long-running/batch models at Spotify and Replicate.

Bento is more flexible – the models can be used outside of Docker, and it has built-in support for deploying to lots of deployment environments, which Cog doesn't have yet.