Shelby
@shelbyserves
Verifiable data platform for distributed compute. Built to make data work wherever compute takes you. Private Beta is live.
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Compute is increasingly dynamic - where it runs, what’s available, what it costs and what fits the workload.
But changing compute often means another cycle of moving, copying, staging or synchronizing data.
Shelby is built to change that - keeping data accessible as compute changes.
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We’ve been heads down hardening Shelby for Private Beta, and we’re beginning to onboard a select group of customers in stages.
Over the next few weeks, we’ll share more about what testnet clarified, how those learnings shaped the product, and the problem Shelby is built to solve as compute becomes more distributed.
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Private production doesn't write itself.
Markets that demand institutional-grade execution. Machines moving faster than any human can. Aptos is the full stack for both.
@DecibelTrade for trading, @shelbyserves for the data layer AI runs on.
$50M+ committed by Aptos Foundation and @AptosLabs:
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Your data, where you need it. No hunting required.
Enterprises don't have a GPU problem. They have a coordination problem: compute in one place, data in another, no efficient path between them.
Building more hardware won't fix that. It compounds the problem.
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$318 billion spent on AI infrastructure in 2025. Less than 2.5% went to storage. (IDC, 2026)
The industry scaled compute.
Nobody fixed how data gets to it.
Last night, one fibre cut in eastern North America sent latency and timeouts as far as Europe, taking major platforms down with it. A single physical fault, a continent-wide blast radius.
Data shouldn't go dark because a cable did. The bytes were fine the whole time.
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Regional storage is as 2021 as a return-to-office mandate. From one place, you can be everywhere.
So why does your data still have to be regional?
Shelby. Write once, read from anywhere on Earth.
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NYT sued. Getty sued. Universal Music sued.
All asking the same thing: what trained your model, and under what rights?
The industry still can't answer. But they could.
Most training data was acquired this way.
We still can't prove what's in the box.
Same object. Four regions. Four bills.
Hyperscalers call this high availability.
You're not paying to store your data. You're paying to store it everywhere it might need to be.
The AI didn't forget you. The storage layer it reads from couldn't keep up.
Shelby breaks this pattern.
Everyone trained on the same internet. The models that matter next won't.
Everything changed when the AI reads attacked.
Only Shelby can save the world.
Hot take: AWS had a bad day. Coinbase needed better failover.
Cold truth: a "global cloud" that breaks when one region overheats is regional infra in a global trench coat.
