Scale Computing Again Recognized as a Leader
Download your free copy of the GigaOm Radar report on Full-Stack Edge Deployments and why they like Scale!
Scale Computing is proud to announce that GigaOm has again named us as a leader and Fast Mover for the second consecutive year. In their most recent report, “GigaOm Radar for Full-Stack Edge Deployments”, the company scored both the capabilities that differentiate solutions (Key Features) and the non-functional requirements that impact the value solutions have to an organization (Business Criteria).
Radar Reports help you develop a picture of the total solution sector, with detailed, rigorous analysis. Leverage their methodology to hone in on the critical criteria and organizational impacts that define the value to your business. And gain the clarity you need to make high-stakes decisions, with the technical backing you need to align organizational support, budget, and resources.
In their latest report, GigaOm examines the key technologies and best practices to consider when evaluating full-stack edge deployments. This report is a valuable resource for IT and business leaders considering the adoption of edge computing.
The GigaOm Radar report highlights:
Why Scale Computing is the ideal fit for any organization of any size
How Scale Computing HyperCore enables seamless programmatic deployment of containers
Examines how Scale Computing Fleet Manager can monitor fleets from one to 50,000 SC//HyperCore-based clusters, including centralized upgrade management and orchestration
Full-stack edge deployments are cloud-managed and cloud-connected hyperconverged solutions that provide all the tools needed to run applications at any customer's preferred location for local data collection and processing. These solutions offer a cloud-like experience at edge locations, which would otherwise be challenging to manage at scale using traditional IT practices.
Deploying applications at the edge also unlocks numerous new opportunities for utilizing AI. Organizations can get new applications up and running in hours rather than weeks. Lower operating costs make niche but potentially powerful AI applications economically viable for the first time.
