Press release

CAST AI Among  The 10 Hottest Cloud Computing Startups To Watch in 2022

As cloud computing usage is rocketing, Kubernetes optimization startup CAST AI promises to solve the issue of soaring costs. The platform is gaining traction with customers and prominent tech media–including most recently CRN.

Despite a relatively short timeline, CAST AI’s full Kubernetes automation and cost optimization platform gets noticed by tech industry media and influencers. The most recent recognition comes from a top brand in the sector–CRN, a prime and well-established source of news and analysis for IT solution providers.

The magazine featured CAST AI as one of the top ten DevOps companies to watch in 2022. CRN listed the platform alongside giants such as Amazon Web Services and Microsoft and other big names in the DevOps space, including HashiCorp, RedHat, and GitLab.

DevOps shortens the development lifecycle, increases agility, and reduces costs, so its significance for businesses has been growing. In this context, the magazine acknowledged CAST AI’s autonomous optimization capabilities and its versatility, noting that the platform supports users of the three key Cloud Service Providers.

The current scale of cloud usage globally makes streamlining the work of engineers an urgent imperative. At CAST AI, we’ve always seen cloud DevOps automation as the best solution to address this challenge. Becoming listed among the top players in the field proves that our product assumptions were right. CRN’s recognition encourages us to continue our efforts for increased cloud affordability and accessibility.

Laurent Gil, CAST AI’s Co-founder and Chief Product Officer.

About Cast AI

Cast AI is the leading automation platform for cloud-native and AI infrastructure. The company achieved unicorn status in January 2026 with a strategic investment from Pacific Alliance Ventures, valuing the company at over $1 billion. Cast AI is trusted by BMW, Cisco, FICO, HuggingFace, and Swisscom to keep mission-critical applications reliable and performant at scale.


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