Cast AI is the leading Application Performance Automation (APA) platform, enabling customers to cut cloud costs, improve performance, and boost productivity – automatically.
Built originally for Kubernetes, Cast AI goes beyond cost and observability by delivering real-time, autonomous optimization across any cloud environment. The platform continuously analyzes workloads, rightsizes resources, and rebalances clusters without manual intervention, ensuring applications run faster, more reliably, and more efficiently.
Headquartered in Miami, Florida, Cast AI has employees in more than 32 countries worldwide and supports some of the world’s most innovative teams running their applications on all major cloud, hybrid, and on-premises environments. Over 2,100 companies already rely on Cast - from BMW and Akamai to Hugging Face and NielsenIQ.
What’s next? Backed by our $108M Series C, we’re doubling down on making APA the new standard for DevOps and MLOps, and everything in between.
PRACTICE CUSTOMER OBSESSION. Focus on the customer journey and work backwards. Strive to deliver customer value and continuously solve customer problems. Listen to customer feedback, act, and iterate to improve customer experience.
LEAD. Take ownership and lead through action. Think and act on behalf of the entire company to build long-term value across team boundaries.
DEVELOP AND HIRE THE BEST. Strive to raise the performance bar by continuously investing in yourself, the team and by hiring the best possible candidates for every position. Drive towards personal development and professional growth, and mentor others to raise the collective bar.
EXPECT AND ADVOCATE CHANGE. Strive to innovate and accept the inevitable change that comes with innovation. Constantly welcome new ideas and opinions. Share insights responsibly with unwavering openness, honesty, and respect. Once a path is chosen, be ready to disagree and commit to a direction.
Autoscaler – Automates Kubernetes node autoscaling to optimize clusters, balance workloads, remove underutilized nodes, and dynamically allocate capacity in real-time, thereby reducing cluster costs by half.
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