I spoke with some development teams who are truly working within the Sui ecosystem. They have a very practical approach when choosing technical solutions: first, they see which option can keep the product alive; the rest are just idealistic notions. In simple terms, it’s about spending less money, responding quickly, and avoiding failures.

Especially for teams that need to handle massive amounts of user data, they are constantly battling with large files like images and videos. Storage costs and data retrieval latency directly impact two things: whether the user experience will be smooth, and whether the server bills will explode. The former determines if the product can retain users, while the latter affects whether the company can survive.

Why is Walrus gradually becoming a choice for developers? The core reason is that it solves the most painful contradiction — ensuring sufficient decentralization without letting costs and speed fall behind. It uses erasure coding technology, which, simply put, compresses data and creates multiple copies, then distributes them across different nodes. The obvious benefits are: storage costs can be reduced by over 70% compared to traditional solutions, and because data is stored in a distributed manner and can be quickly reconstructed, the storage layer won’t become a bottleneck that drags down user experience during traffic surges.

What’s even more attractive is its deep integration with the Sui chain. Contract calls to data on Walrus feel as smooth as reading local files, with no cumbersome cross-chain verification friction. As a result, development efficiency increases, user experience becomes more seamless, and operational costs are kept under control — three factors that are usually mutually restrictive are stitched together here.

When developers start voting with their actions, the winners are often those solutions that allow them to focus on product innovation rather than being troubled by infrastructure issues.