On Friday evening, I received an urgent notice—requiring us to migrate 1TB of AI training data from centralized cloud services to a decentralized solution within 72 hours. My initial reaction was one word:绝. But by the time Monday morning arrived and the data was fully on-chain and accessible, I realized something interesting—throughout the entire crypto world, very few people seriously discuss the most fundamental requirement: the data layer.

The 1TB we have is not virtual. Three months of AI model training results: millions of labeled images, hundreds of thousands of hours of audio data, plus a bunch of complex model checkpoints. All of it was neatly stored in the company's AWS account, but now the project is shifting to community governance, which means the data must be accessible and verifiable by contributors worldwide.

**The First Pitfall of IPFS**

Our first instinct on Friday night was to use IPFS. It sounded perfect—distributed storage, content addressing, inherently resistant to censorship. But what was the reality? Less than six hours into the migration, cost issues brought us back to reality. Maintaining availability of 1TB data on IPFS requires constantly "pinning" it to prevent garbage collection. Once fixed costs from mainstream pinning services came out, it was way over budget.

Even more painful was the speed. Theoretically, IPFS is globally accessible, but actual access experience depends on network topology and node caching. Our community members are spread across five continents—some can access data in seconds, others have to wait ten minutes or more. For AI model development that requires frequent iterations, this is an intolerable bottleneck.

**Filecoin’s Promise vs. Reality**

By Saturday noon, we switched to Filecoin. Theoretically, storage costs are lower—in official data, 1TB monthly fees can save us a lot of money. The problem is complexity. Filecoin’s storage relies on miners’ commitments, and retrieval requires paying additional fees to retrieval miners. This means we need to balance storage costs and retrieval costs. Also, retrieval prices vary significantly across regions, making cost predictions less straightforward.

More critically—although Filecoin is decentralized, its storage model is essentially "pay miners to hold data." For applications that require ensuring data integrity and availability verification, this trust relationship is somewhat passive.

**The Turning Point on Saturday Night**

A peer mentioned Walrus. Honestly, I hadn’t heard of it before. After reading the documentation, I understood—it’s infrastructure designed specifically for application-layer data. It employs a different logic: through Byzantine-robust storage proofs, it guarantees data availability and integrity. Simply put, the system has built-in cryptographic validation of data validity, not relying on "trusting miners," but on mathematical guarantees.

What attracted me most was its design philosophy—acknowledging what decentralized applications need most. It’s not about fancy consensus mechanisms or flashy governance tokens, but about: usability, affordability, and verifiability.

Starting Sunday morning, I used Walrus’s SDK to migrate. The entire process was surprisingly smooth. Uploading data, generating Blob IDs, integrating into the application layer—done in three or five hours. The key was that the access latency reported by global test users finally stabilized, and the cost model became very clear.

**The True Revelation**

After completing the migration and reflecting on these 72 hours, I realized a serious overlooked truth: in the Web3 world, most discussions focus on consensus algorithms, governance mechanisms, tokenomics. But whether a decentralized application can truly run depends on whether the data layer is usable.

IPFS is elegant but its cost model is unfriendly. Filecoin attempts commercialization but is too complex. Walrus’s emergence points to a direction—perhaps the future of decentralized infrastructure isn’t about whose tech is the coolest, but about who truly understands the pain points at the application layer.

Returning to our AI model project: now community contributors can confidently verify dataset integrity, access training materials at reasonable costs, and be assured that the data won’t suddenly become unavailable due to a node going offline. This sense of reliability is something I’ve never experienced with many other Web3 tools I’ve used before.

Maybe this is what infrastructure should look like—not flashy, but focused on solving real problems.