High-Performance Data Platform for AI Anywhere

Hammerspace is the data platform for AI Anywhere - built to deliver results, not replatforms. Hammerspace Data Platform is a high-performance, Linux-native, standards-based data platform that unifies unstructured data across sites and clouds and feeds GPUs at full speed with Tier 0 performance. Hammerspace is designed for enterprises building AI factories across on-prem and cloud storage, with a goal of keeping GPUs utilized, making petabyte-scale storage AI-ready, and want to use their existing storage, networks, and clouds. Built on open standards, Hammerspace reduces copy sprawl and egress and turns existing infrastructure into a high-performance, unified AI data plane

Unified Data Plane

Seamless read/write access to data on any storage, anywhere Standards-based means there is nothing to install on the client side. Users/applications simply access their complete data estate across all storage silos, sites, and clouds, to support general file access and extreme-performance parallel file system workloads for AI.

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ping identity fedramp high authorized iam platform

Global Data Services

Simplify data placement, protection, and governance with a single set of policies. Hammerspace assimilates metadata from your existing storage in place to simplify metadata-driven policy objectives, automate data placement, define data protection levels, and migrate and/or tier data for AI inferencing and other uses.

Data Orchestration

Remove the complexity of multi-silo, multi-site data policy actions - even on live data that is in use. Automated data orchestration executes policy-driven actions across all storage silos—spanning sites and clouds, to enable multi-site collaboration, accelerate AI use cases with Tier 0, perform tiering, migration, and other policies completely in the background.

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ping identity fedramp high authorized iam platform

Accelerate AI Use Cases with Tier 0

Leverage local NVMe in your GPU servers as an ultra-fast, shared storage tier. This allows you to feed GPUs with maximum throughput, keeping them fully utilized at PCI bus speeds and enabling linear scaling for large-scale training and inference.