Cast AI

“It took us a month from meeting the Cast AI team to closing the POC. We’ve rolled out the solution gradually over three or four months, initially on lower environments and not production clusters. Everything was working as expected, which gave us the confidence to move forward. We achieved savings practically immediately, so our ROI timeline was very short.”

“We were impressed with the expertise and research done by the Cast AI team. We have some technical experts on our team who are capable of realistically judging different solutions. I think everyone was quite impressed with Cast. We were looking to achieve results in weeks and months, not years. Once we completed the POC with Cast, we could confirm that this was the right vendor for us. Getting a new budget was easy since we had a very strong argument based on cost reduction.”

“Obviously, we could achieve some of that using native tooling from cloud providers. But we knew that it would take massive effort to make it work internally. So, we started looking for something better. We did a bake-off and assessed a few vendors, considering security, reliability, and speed of achieving savings results.”

“We are a high-growth company and need to be careful about prioritizing our work. One area we wanted to focus on was getting better and more granular data about our infrastructure costs. We’ve been struggling with getting a bottom-up, granular view of how Kubernetes resources are being used and whether we are reporting them accurately.”

“Since everything is managed automatically by Cast without any manual intervention by engineers, our team has saved a lot of time. Given that we’re a startup in a really challenging market, the ability to reallocate resources to other, more innovative topics is essential to us. Our DevOps team is small, so their time is precious.”

“Positive impacts were directly visible once we integrated Cast. The platform optimizes our costs by selecting the most cost-efficient compute instances. Using the Cast autoscalar didn’t impact our availability.”

“Cast AI is a good “companion” for using Kubernetes in a cloud environment. Its reporting and monitoring interface is great and easy to use. It provides a good solution to optimize costs, is easy to integrate, and delivers quick results. Cast AI is the orchestrator of our orchestrators.”

“Given that we’re a startup in a really challenging market, the ability to reallocate resources to other, more innovative topics is essential to us. Our DevOps team is small, so their time is precious.”

“At first, we relied on Google Cloud Platform’s native autoscaler, followed by some manual adjustments. However, this approach simply wasn’t sustainable, as it was impossible to manage our infrastructure in real time, and the costs left room for optimization.”

“The objective is to strike a balance between performance and cost to avoid overprovisioning while simultaneously delivering optimal service to customers.”

“Cast AI is an all-in-one solution that delivers all the optimization and reporting features we need: 1. Cost visibility and monitoring via detailed dashboards, 2. Ability to optimize costs using the autoscaler and automated instance selection, 3. Spot VM automation with fallback management. On top of that, it was simple to implement and easy to integrate into our infrastructure.”

“Machine learning models are expensive to run in the cloud, so we’re looking at the possibility of using Cast AI to reduce costs in this area. We expect that it will help us deal with scale and responsiveness, it’s a big part of our strategy. Having that strong client-supplier relationship is reassuring, meaning that Cast will be there to support our needs as they evolve.”

“I can’t think of a company that wouldn’t benefit from CAST AI. Maybe a hobbyist running a single node, or someone like that. But otherwise, the advantages are clear. A startup can benefit from getting a lower cloud bill and using smarter cost management. For giant enterprises, CAST AI could save millions of dollars per month. It just seems to be a no-brainer for me.”

“With Cast AI, it’s pretty much just an annotation, and then the solution will act on its own, buying more resources in line with our requirements. This makes the lives of our engineers easier.”

“One cultural benefit we got from Cast is more information and more focus on costs in general. I’ve always struggled with making cost everyone’s problem. Having a tool that supports that and feeds the information to different people is a helpful reminder about the importance of costs and why we have to focus on them. At the end of the day, we’re a business that needs to make money.”

“I know this sounds terrible, but I can’t remember the last time I logged into the Cast AI console. For us, Cast has really been a plug-and-play setup. It’s stable and it saves us a ton of money. As long it does that, it’s a perfect product and there’s no need to interact with it.”

“The onboarding and support have been great. As an engineer, I appreciated the opportunity of being able to speak to the engineers directly. It’s been an experience I’ve not had before. It felt like they’re like a separate engineering team and they’re working along with you to build this overarching solution for your clients. It’s great having such an open culture, which cannot be said about many other suppliers,”

“Obviously, for a pharma company, it makes a big difference when a solution is available through a channel like Azure’s AKS Marketplace. We have a strict process around supplier approvals that covers aspects like risk assessment, validation, and ensuring that the product matches the requirements and compliance standards. If we’re operating within the Azure ecosystem and all the data, billing, and setup stay within Azure, it certainly makes things a lot easier because a lot of risk stays within Azure and not with the supplier.”

“And ultimately, if a solution passes all your tests, it means that it’s a good fit. In this case, Cast AI ended up saving us a lot of money without causing any issues whatsoever.”

“Those guys were very passionate and clear about what they wanted to do. So it was nice dealing with the founders and engineers directly, so I never felt the need to look elsewhere. I believed in their product and what they were doing. I reviewed one other cloud automation product but it just didn’t fit that kind of feature suite that Cast AI offered,”

“The solutions that I really like are plug-and-play – you plug them in and then can forget about them because they get the job done. Having something that just takes away another piece of complexity in someone’s day is really important to me. I want it all to be automated, I don’t want to have to deal with it on a day-to-day basis. And Cast AI has been perfect for that, it’s now an integral part of our toolkit.”

“Looking at our long-term vendor relationship, we feel like there could be ways of incorporating and improving our carbon footprint using Cast and the decisions it makes,”

“I feel like Leon [Cast AI’s CTO] is driving lots of interesting product features forward. That was another reason why we signed up. While Cast is already valuable as a product, I’m even more excited about what’s to come,”

“When I look at the autoscaler settings in Cast, it checks many boxes I’ve been developing. So many of these policy features are achievable. Outside, they probably need three to five components that work together and are often unaware of each other. In Cast, it’s just a matter of configuring the policy and watching how it performs – necessary data is also presented to you ,”

“Cast feels like a very modern approach both from the technical and UI perspective. Add to that excellent support team resources to answer any questions, and you’ve got the three biggest things that stand out for me. It feels like driving a Tesla.”