The popularity of generative AI has brought an unavoidable question—how to store, how much to store, and how to manage it?

Basically, there are three requirements: affordability, security, and ease of use. Training datasets pile up, generated content needs archiving, and data permissions must be managed meticulously. These should be minor issues, but they have become the main bottleneck hindering the large-scale deployment of AI applications.

It’s worth mentioning that Walrus Protocol has already made significant inroads in this field. Providing infrastructure support from the storage layer for AI projects is a pretty interesting approach.

Take the generative AI platform Everlyn as an example; this case is quite representative. Everlyn uses Walrus as the data layer, storing over 50GB of training datasets, model checkpoints, KV caches, and other core data. All newly generated high-quality video content is also stored on Walrus. Why choose this approach?

The core reason is quite straightforward—cloud services like AWS and Azure see their storage costs soar as video generation demands increase. Walrus’s Red-Stuff encoding technology, combined with the Quilt batch storage scheme, can reduce storage costs to a fraction of traditional solutions, without sacrificing data high availability and access speed. This allows AI developers to focus more on model optimization rather than being drained by storage bills.

In terms of data management flexibility, Walrus also demonstrates its strength.