Volumez - AI Data Platform
Software defined storage foundation for high-performance data workloads, enterprise AI and agents infra delivering native GPU Direct, shared KV-cache acceleration, inline ingestion and in-place processing on a single copy of your data.
Enterprises
Defense · Finance · LogisticsGPU Clouds
NeocloudsAI Inference Providers
Token-serving at scaleSaaS & ISVs with own AI
Product-embedded modelsSovereign AI Clouds
National · RegulatedEvery bottleneck ends the same way
Organizations keep optimizing systems never designed for today's workloads.
Same demand, two very different curves
Cost, Complexity, Performance and Overall Experience
More hardware than the work requires
More nodes for performance and a separate system for every workload, each adding racks, power, cooling and licences while extracting less from every petabyte.
Inference state takes the long way to the GPU
A lookup, a software layer and a memory hop sit between the state and the GPU, and every extra step pushes time to first token up under load.
Fragmented data is delaying your AI readiness
Data sits in silos with its own copy and access path, so making it AI-ready means stitching, parsing, embedding and indexing it into yet more systems and pipelines.
GPUs recompute work you already paid for
Session state outgrows GPU memory and is evicted with nowhere fast enough to fetch it back, so the cluster rebuilds context it has already processed.
It's possible to reach superior performance on far fewer resources using an architecture built for performance and efficiency.
The Volumez way
Built from the bottom up on purpose.
Storage, data workloads, enterprise AI and inference on one underlying copy of the data. Fewer nodes doing more work, with the throughput, IOPS and concurrency the same infrastructure never delivered.
Four capabilities, one foundation
Each one removes a copy, a hop or a rebuild from the path between your data and the compute that needs it.
Data workloads
Compute where the data lives.
Heavy parallel processing – ETL, Spark, analytics – runs on the same nodes that hold the data. Nothing is copied out to a compute cluster, processed and shipped back. With less CPU spent on the I/O path, more compute is available, delivering more throughput from the same infrastructure.
Enterprise AI
Reload instead of recompute.
Session state stays reachable across the entire GPU fleet – fetching beats redoing. Time to first token drops, long sessions stay as fast as short ones, and freed compute goes back to serving more users. GPU-agnostic: the same tier, whatever the fleet runs.
Agents infrastructure
Agent-ready the day the data lands.
Ingestion, indexing and vector search happen as data arrives – kept hot, queryable by whatever asks. The index lives with the data itself, so nothing needs a second system and nothing falls out of sync.
Storage & infrastructure
Placement computed, not searched.
No metadata tier and no central lookup for data placement – where data lives is a path equation. That removes a scaling bottleneck: throughput and IOPS scale linearly with nodes, latency stays flat as clients grow, and the same performance target needs less infrastructure.
Enterprise AI, GPU Clouds, HPC…
Teams whose economics are set by how well their infrastructure keeps expensive compute busy.
GPU cloud providers
Sell more GPU-hours from the same fleet. A shared KV-cache tier and a GPU Direct path keep accelerators fed, and one storage layer serves every tenant workload instead of a stack per service.
Enterprise AI platform teams
Run training, inference, analytics and retrieval on one copy of the data, behind the protocols your estate already speaks – no separate system per workload, no nightly pipeline to keep in sync.
HPC and data-intensive research
HPC-grade throughput and concurrency on commodity nodes, with linear scaling as nodes are added and flat latency as client counts grow.
One foundation for data workloads, agents, inference, retrieval and vector search – all operating on the same copy of your data.