Wildbit Selects InterModal Data

InterModal Data, Inc., provider of disaggregated storage software solutions purpose-built for the enterprise, announced that Wildbit, LLC selected InterModal Data as its storage solution to underpin Wildbit’s developer and designer software offerings.

Upon deploying InterModal Data’s software storage solution on existing hardware, Wildbit realized a 2X performance increase over its previous solution, with additional performance increases expected.

“As our products and infrastructure continues to grow, we needed a storage solution that provided enterprise reliability and availability that could easily scale in both capacity and performance,” said Chris Nagele, CTO, Wildbit. “With InterModal Data, we get a scalable, flexible and efficient storage solution that delivers significant advantages in both price and performance, allowing us to expand our infrastructure in line with our product growth.“

Looking to leverage its existing hardware supplier and supply chain, Wildbit needed a storage solution that could scale and handle the capacity and performance needs to support a virtualized environment. InterModal Data’s dis-aggregated storage software solution allows Wildbit to utilize resources to meet performance and capacity requirements as needs arise.

“In today’s storage market, the challenges of scalability, efficiency and flexibility are far too common,” said Dave Kresse, CEO, InterModal. “We’re excited to be working with a forward thinking company like Wildbit to help solve these issues and continue to grow its business.“

InterModal’s distributed storage software solution can scale to support millions of workloads and billions of files. The software allocates resources on a per-workload basis in order to meet specific performance requirements, while using the lowest cost resource. The disaggregated architecture physically separates the storage controllers from the shelf, creating data nodes and storage nodes that are logically connected over Ethernet. The software governs the use of RAM and flash caches on every node, optimizing the ability to service the active data set. When conditions or requirements change, the software reallocates resources across the Ethernet fabric to meet workload requirements.