AI training and datasets
Training corpora, embeddings, and feature stores moved to wherever the accelerators are, then moved again when the next run needs a different region.
05 / Cloud to Cloud
TransBlur moves data between AWS, Azure, Google Cloud, Oracle Cloud and the rest, on private paths built for width. POPs sit in every major cloud, so a transfer starts close to you and lands close to them.
01 / Transfer shapes
Training corpora, embeddings, and feature stores moved to wherever the accelerators are, then moved again when the next run needs a different region.
Weights, checkpoints, and exported builds crossing between the cloud that trained them and the cloud that serves them.
Lift a working environment into another cloud inside a window measured in hours, with the path destroyed once the cutover is done.
Keep a second cloud warm. Repeatable paths, built fresh each time, with nothing accumulating between runs.
Re-shard, re-key, or rebuild a warehouse across providers without the transfer becoming the schedule risk.
Source material and finished cuts crossing clouds on the way to a hard delivery date.
02 / Capacity
If a transfer we have committed to misses a figure stated here, the refund is full and unconditional.
03 / Reality check
Capacity is ours to supply. The rest is decided together, before the transfer, not discovered during it.
A transfer starts close to you and lands close to them, but physics still sets the floor. Intercontinental paths are provisioned differently from metro ones.
The stated figures assume both ends are provisioned properly. We check before a path is built rather than after a transfer runs slow.
Disk, object-store throughput, and the network at each end cap what a path can use. A wide pipe cannot outrun a slow source.
Referral only
Your referrer contacts Customer Support or Billing with the volume, the source, and the destination. Nothing is promised until the path is understood.