Hammerspace has launched a hybrid cloud data platform that unifies fragmented enterprise data across multiple environments without forced migrations. The solution activates existing server storage to accelerate GPU performance while automating data movement, helping organizations overcome infrastructure barriers preventing AI projects from reaching production.
-- Hammerspace Data Platform has announced the launch of its hybrid cloud data platform, designed to address a common barrier that keeps enterprise artificial intelligence initiatives from reaching production: scattered data across systems and difficulty delivering consistently at scale.
More information is available at https://hammerspace.com/lp/why-most-ai-projects-stall-before-production/
The announcement comes as many enterprises struggle to transition AI initiatives from pilot to production. While organizations often invest heavily in GPUs, specialized talent, and high-performing models, production environments require continuous, scalable access to vast volumes of unstructured data. When that data remains fragmented across storage systems, cloud platforms, and geographic regions, deployment slows regardless of model quality or compute capacity. Gartner estimates suggest that approximately 60% of AI projects fail to reach production due to persistent data fragmentation, delaying timelines, and reducing return on AI investments.
The Hammerspace Data Platform approaches this challenge at the data layer. Among its key features is the Global Namespace, designed to unify unstructured data regardless of location or underlying storage technology. From the perspective of an AI workload, data across on-premises data centers, multiple clouds, and edge environments appears as a single, coherent space, allowing teams to run workloads where it makes the most operational sense instead of reorganizing data first.
A capability called data assimilation emphasizes visibility and selective movement. Enterprises can rapidly view and access millions of files across geographies and protocols, then move data only when processing genuinely requires it. This approach reduces duplication, limits exposure created by unnecessary transfers, and provides immediate insight into what data assets exist for model training and inference.
To keep that unified view governed, the platform adds automated orchestration driven by metadata-based policies. Organizations can define rules in plain language that determine where data resides, how it shifts between tiers, and when it should be archived. Policy-driven automation replaces manual data movement tasks that slow teams down and introduce inconsistency.
Built on open standards, including NFS, SMB, and S3, the platform is intended to integrate with existing infrastructure and avoid vendor lock-in. Organizations can begin on current systems, extend workloads into cloud resources as needed, and deploy inference at the edge, all managed through a single data layer.
As a Hammerspace spokesperson explained, “Organizations invest heavily in GPUs and AI talent, and most initiatives stall because the data layer underneath cannot deliver information consistently or efficiently. The solution is not more storage or additional data copies, rather a fundamental shift in how enterprises manage their data infrastructure to support AI at scale.”
The Hammerspace Data Platform is now available for deployment across on-premises, cloud, and hybrid environments. Interested organizations can learn more through an on-demand webinar available on the company’s website at https://hammerspace.com/lp/why-most-ai-projects-stall-before-production/
Contact Info:
Name: Hammerspace
Email: Send Email
Organization: Hammerspace
Address: 1900 South Norfolk Street, San Mateo, CA 94403, United States
Website: https://hammerspace.com/
Source: NewsNetwork
Release ID: 89185099
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