VAST Data
VAST DataSpace
IntermediateA global namespace that makes the same data addressable across edge, on-prem and every cloud - one source of truth, strictly consistent, with no copies to keep in sync.
The data-gravity problem
AI workloads now run wherever GPU capacity is available - on-prem, in one cloud this week and another the next, out at the edge where data is generated. But the data has gravity: it is large, it is expensive to move, and the usual answer is to copy whole datasets to every site that needs them. Those copies multiply storage and egress cost, and they drift out of date the moment one site writes.
VAST DataSpace is a global namespace - a data fabric that unifies many VAST clusters worldwide into one consistently addressable space. The same files, objects and tables are reachable from every site at local all-flash speed, and data moves only when it is actually needed.
Data gravity
Datasets are too large and costly to shuttle between sites on demand, so compute ends up waiting on staging.
Copy sprawl
Replicating to every location multiplies storage and egress cost - and every copy is a chance to read stale data.
Multi-site by default
Training and inference span on-prem, multiple clouds and the edge - they need one view of the data, not many.
One namespace, no copies
Instead of pushing a full copy of the dataset to every site, DataSpace keeps one authoritative copy in the namespace and lets each site stream only the bytes it touches - or sync metadata only, moving no data until it is read. Toggle the two models to see the difference in copies, consistency and data movement.
Edge
streamed slice
AWS
streamed slice
Azure
streamed slice
Google Cloud
streamed slice
1 source
Copies of the data
Strict
Consistency
On demand
Data moved
DataSpace: one source, streamed on demand
One authoritative copy lives in the namespace. Sites cache locally and stream only the bytes they actually touch - or sync metadata only, moving no data until it is read. One source of truth, no drift, and you pay to move data once, when it is needed.
How global access stays consistent
VAST is blunt that “eventually consistent isn't consistent enough.” DataSpace uses an Origin / Satellite model with decentralized read and write leases. Per global folder, one cluster is the Origin that holds the authoritative copy and the write lease; the others are Satellites that cache locally under read leases. Step through a remote write to see why an update in one site is visible everywhere - with no eventual-consistency lag.
Origin · authoritative copy
holds the write lease
Satellites · local flash caches
read leases
One namespace, one authoritative copy
Per global folder, one cluster is the Origin - it holds the authoritative copy and the write lease. Every other site is a Satellite that caches the data locally on flash under a read lease, so the same file is addressable everywhere at local speed.
Roles are per-folder, so a single cluster can be Origin for some data and Satellite for others. A remote write costs a round-trip to the Origin, so VAST recommends placing the Origin near write-heavy clients; write leases are also becoming portable so they can live where data is created and migrate afterward.
Caching, prefetch & intelligent streaming
Each global folder has its own cache capacity and prefetch policy, so admins decide how aggressively a Satellite warms its local flash. Data is moved only when required - the namespace is global, the bytes stay put until something reads them.
On-demand (default)
Satellites fetch and cache data the first time it is read, then serve it locally on flash for subsequent reads.
Full prefetch
Warm the entire dataset into a Satellite ahead of a job, so the first read is already local - no cold-cache penalty.
Metadata-only
Sync just the namespace structure - file and object names appear everywhere while the actual bytes stay at the Origin until touched.
Snapshots & replication
The same write-in-free-space design that powers the platform makes data protection cheap across the fabric. Snapshots take no data or metadata copy, and replication ships only the byte ranges that changed.
Near-instant snapshots
A single cluster supports up to ~1,000,000 snapshots with no data or metadata copy - point-in-time views for free.
Byte-delta replication
Fine-grained change counters mean replication ships only changed byte ranges, in sub-one-minute intervals.
Sync or async
Choose synchronous replication for tight RPO or asynchronous for distance - per folder, across the fabric.
Global clones
Spin up a consistent clone of a dataset anywhere in the namespace without duplicating the underlying data.
Data follows the GPUs
Because one namespace spans every site, jobs can be scheduled wherever accelerators are free and stream the data they need from the Origin - rather than forcing a dataset migration first. Training runs across sites against a globally consistent view, checkpoints land without stalling GPUs, and inference deploys close to where data is generated with embeddings kept current for RAG.
In a VAST-reported demonstration, DataSpace connected clusters roughly 10,000 km apart (US and Japan) - with TPUs on one side and GPUs on the other - sharing one namespace. Cloud instances run natively in AWS, Azure and Google Cloud, and as of November 2025 DataSpace ships as a fully managed VAST AI OS service on Google Cloud with TPU support.
Schedule on free capacity
Send the job to whichever site has GPUs or TPUs available; the data streams to it on demand.
Multi-cloud reach
Ephemeral cloud caches for burst, or persistent instances that mirror writes for durability - across AWS, Azure and GCP.
Fresh context for AI
One consistent view keeps features and vector embeddings current everywhere, so RAG and agents never read stale data.
The 10,000 km demo, ~1M snapshots, sub-minute replication deltas, and the Nov 2025 Google Cloud availability are VAST-reported (company announcements and platform docs) and subject to change.