Cloud-friendly file backup, visibility, and archive for

High-Performance Computing (HPC)

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Igneous for HPC

Igneous delivers a high-performance unstructured data management solution for high-performance computing (HPC) environments.





Legacy data solutions can't keep up

HPC users generate the largest file datasets in the world - the sheer size of the data makes understanding and managing it very difficult. Not to mention the high cost of performance storage makes keeping all that data on primary storage impossible.

Protecting petabytes of data and billions of files is difficult, if not impossible, for traditional solutions to handle. As a result, many organizations have turned to bespoke solutions that requires constant management and oversight.

In a HPC environment it’s critical to understand which projects are complete, and which can be archived to free up valuable primary NAS capacity.

That's why Igneous was built.


SaaS backup, archive, and visibility designed for scale

Today’s HPC workflows require a modern data solution, optimized for the scale of the enterprise.

Igneous is architected for the densest file workflows and petabytes of data. Make intelligent data decisions with better visibility, archive old projects, and restore on-demand. 

With Igneous you can protect data on-premises using commodity hardware, or leverage the cloud for cost-effective secondary copies of data at the scale and speed of HPC. Even under high change rates, Igneous hits the toughest SLAs without any customization required.



AdaptiveSCAN, IntelliMOVE and InfinitieINDEX are the foundation to seeing, moving, and indexing data faster than anyone else


Designed to handle massive amounts of data across both performance and capacity tiers, delivering maximum scalability of large datasets

Deployment Options

With Igneous protect data on-premises or in the cloud

Data-Heavy Teams Use Igneous to:


Our Products



Real-time indexing, global visibility, and powerful search

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Protect petabytes of data and billions of files

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Modern workflows shouldn’t have to wait for data

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