cast.ai Raises $7.7M Seed Funding - Public Cloud Optimization

Deploying an application to the public cloud often involves committing to a single provider, but what if you could select the most suitable components based on both cost and technology, distributing your database across one platform and your storage across another?

Cast.ai proposes a solution to this challenge and has recently announced a $7.7 million seed funding round led by TA Ventures, DNX, Florida Funders, and several other angel investors to further develop its capabilities. This funding round was finalized in June.

According to Yuri Frayman, CEO and co-founder of the company, Cast.ai was founded on the principle that developers should benefit from the strengths of all major public clouds without being constrained by vendor lock-in. They achieve this by building Kubernetes clusters capable of operating across multiple cloud environments.

“Cast simplifies application deployment by requiring minimal user intervention. You are not required to be aware of the underlying cloud infrastructure during runtime. The only necessary steps are identifying the application, specifying the desired cloud providers, defining the allocation percentage for each provider, and initiating the launch,” Frayman clarified.

This approach allows for the utilization of services like Amazon’s RDS database alongside Google’s ML engine, with the platform intelligently determining the optimal configuration based on your specified requirements and budgetary constraints. You establish the desired policies during launch, and Cast.ai handles the distribution of your application to the chosen locations and providers, or those that best suit its needs.

The company leverages cloud-native technologies, containerization, and Kubernetes to overcome the inherent limitations between different cloud platforms, explains Laurent Gil, co-founder of Cast.ai. “We dismantle the barriers imposed by cloud providers, enabling applications to operate across multiple environments simultaneously. This is particularly advantageous for Kubernetes applications, which are inherently designed with this level of flexibility in mind,” Gil stated.

Developers utilize a policy engine to determine the extent of their control over this process. They can opt for automated optimization across clouds by simply specifying a geographic location, or they can precisely select the resources to be used on each cloud provider. Regardless of the chosen method, Cast.ai continuously monitors the deployment and optimizes it based on cost, ensuring the most economical options are utilized for the given configuration.

Currently, the company employs 25 individuals, with four additional hires planned, and aims to expand its team to 50 by the end of 2021. As the company grows, it is prioritizing diversity and inclusion in its recruitment efforts, currently having women leading its HR, marketing, and sales departments.

“We have implemented comprehensive, ongoing education programs within our organization focused on diversity training. Many of us have experience from organizations where diversity was a key priority, and we have incorporated those valuable processes and insights into our company culture,” Frayman noted.

Frayman has a background in several startup ventures, including Cujo.ai, a consumer firewall startup that competed in the TechCrunch Disrupt Battlefield in New York in 2016.