Gigantum Revenue and Competitors

Data science in hybrid and multi-cloud settings? Yes. It is possible. In fact, it is easy. We built the easiest and most cost effective way for data science teams to work and collaborate on hybrid and multi-cloud infrastructures. We did this by taking a fundamentally different technical path than the rest of the crowd. Rather than rely on centralized, monolithic deployments, we followed the principle of "decentralized work with centralized sharing." We built a system that lets anybody work wherever they want without losing the ability to share and collaborate in a uniform and intelligible fashion. This is the original approach that scaled collaboration for software devs via Git and services like GitHub and GitLab, and we knew it would do the same for data scientists if it were properly containerized, automated, and streamlined. Rather than rely on a chaotic Python packaging ecosystem, we created a containerized web app (the Client) that handles local work, including development and computation. Then we paired it with a simple self-hosted storage Hub that provides easy work transfer and syncing between different users and machines. The result is a user-friendly SaaS experience that enterprise can have on whatever collection of resources they want, not just the infrastructure dictated by the platform vendor. Data scientists can work in a self service fashion wherever is required while still maintaining full reproducibility and portability across all other users and contexts. Gigantum can be easily deployed across a wide variety of contexts, not just a single commercial cloud. It eliminates technical headaches for coding data scientists while providing a cost effective deployment solution for enterprise.