CTO.ai serverless platform simplifies adoption of Kubernetes for developers

CTO.ai announced the launch of its serverless Kubernetes platform that makes it easy for developers to deploy and manage their cloud native applications. This powerful, yet easy-to-use, platform makes product delivery teams more efficient and eliminates the complexity experienced by developers when applications are deployed on top of a self-managed Kubernetes cluster.

The CTO.ai platform was created to address the estimated $300 billion lost in developer productivity every year, much of which comes from complex modern cloud tooling.

Software teams typically spend inordinate amounts of time and money creating their own custom workflow tools or must buy several specialized tools that need to be integrated to make managing Kubernetes easier for their developers.

The CTO.ai platform eliminates this issue by enabling software developers to easily adopt Kubernetes at any stage of development, without the exponential cost of retraining, hiring, or manually building the developer workflows needed to operate Kubernetes efficiently.

Startups can use the platform to host a monolith or microservice based application; enterprises can layer the platform into their own private cloud cluster using a service mesh.

“Our platform is the first in the Kubernetes market to go to this length in prioritizing a complete and intuitive experience for developers that allows operations teams to still harness the full capabilities of Kubernetes.

“We created it so that developers can focus on actually developing applications, not operations,” said Kyle Campbell, founder and CEO of CTO.ai. “We love Kubernetes, but anyone with experience knows that it can be complex to manage and scale.

“Our platform addresses this by providing software teams of any size with an unmatched “PaaS-like” developer experience for their cloud native application development workflows.”

The CTO.ai platform enables developers to easily manage all of their CI/CD, staging, and production systems on top of a shared Kubernetes cluster, without forcing them to manage Kubernetes directly.

As deployments scale, companies have the option to offload their cost of compute to public cloud providers like AWS via the CTO.ai service mesh, which enables them to bring down their cost to scale and internalize their infrastructure operations directly.

The platform also monitors delivery workflows and provides stakeholders delivery insights so they can better understand their operational cadence for estimation or planning.

Insights are calculated in real time from events that happen across their workflow systems and this enables teams to measure important metrics like deployment status, velocity, and stability.

The CTO.ai mission is to simplify adoption of Kubernetes for developers, enabling them to make use of world class tooling but without being forced to compete for scarce DevOps talent to do so.

From startups to enterprises, development teams at all levels can benefit from the efficiencies delivered by the CTO.ai platform. It enables a consolidated strategy for delivery systems and provides data-driven insights into delivery cadence.

Optimized for fast-paced product delivery, the CTO.ai platform helps startups speed time-to-market since it doesn’t require the support of hard-to-hire DevOps engineers.

Enterprises can accelerate their digital transformation and lower their cost of digital teams by consolidating their delivery systems and adopting a data-driven approach to development.

CTO.ai has been developing this unique platform over the past three years with support and input from the DevOps teams at venture funded startups such as Axial, TrueBill, Cedar, YellowDig, and Remine, as well as others in the DevOps community.

“Having this technology available is going to make it easier to develop dev operations around new systems, and it’s definitely going to be a big advantage for us going forward,” said Zack Weheim, director of engineering at Axial.

“Without CTO.ai, we would have to adopt the bare minimum tooling. Anything new would have to be written in a language our team was familiar with and we’d be running scripts manually.”


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