AI teams now plan around compute. More workloads depend on specialised hardware, and that hardware is not unlimited, so teams reserve capacity, move between providers and make decisions based on what is available, where, at what cost and for which workload.
That makes compute both more valuable and more dynamic, and makes efficient use of the capacity you have more important.
The data those workloads depend on is comparatively rigid. When the best place to run a workload changes, the data path can make the move slower, more expensive or operationally difficult. Sometimes it even decides whether the move happens at all.
Shelby is built to reduce that friction: making data work wherever compute takes you.
The infrastructure gap
More compute availability should create more choice.
But that choice only matters if workloads can actually use it.
Today, bringing a new compute environment into a workload often means doing additional work around the data first.
A new provider can mean another data endpoint. A different location can mean another staging process. Making a dataset available to another workload can mean provisioning and maintaining another full copy.
The cost is not only transfer or storage. It can also show up in staging time, idle compute, synchronisation work, new permissions, pipeline changes and the operational burden of keeping multiple data paths usable.
At small scale, those decisions are manageable. As datasets and infrastructure footprints grow, they become part of the architecture.
In practice, that can mean a newly secured block of GPUs waiting while data is staged, or a better capacity offer passed over because moving the data is not worth the effort.
The result is that where the data already sits can start shaping the compute decision.
The principle behind Shelby
The data layer should enable compute decisions rather than constrain them.
Better compute is only useful if the workload can actually take advantage of it.
Data infrastructure therefore has to be designed around a different assumption: compute will move.
It should be possible to change where a workload runs without rebuilding the data path around every decision. The work of moving should never outweigh the benefit of better compute.
Why now
The compute market is becoming more heterogeneous.
Hyperscalers, neoclouds, GPU clouds, data centres and specialised infrastructure are creating more ways to match infrastructure to a workload based on availability, geography, architecture and economics.
AI is also changing what workloads need from the data layer. GPU infrastructure is evolving quickly, agent interactions create new access patterns, and workloads increasingly span providers and environments.
Those changes create a broader role for the data layer. It needs to support workload patterns that are evolving alongside the compute itself.
The more compute fragments across providers and locations, the more valuable it becomes to have data infrastructure that can keep up.
We built Shelby for that shift.