Question: Is there a solution that can predictively determine the right resource settings for containers and pods in Kubernetes?

If you need a tool to predictively figure out the best settings for containers and pods in Kubernetes, Densify is a good option. It combines machine learning and deep analysis to find the best settings for performance and cost, a big help for problems like underutilization and badly tuned auto-scaling. With features like Kubernetes resource control and auto-scaling group optimization, Densify can help you squeeze more out of your resources and cut costs. It also fits into existing operations like CI/CD pipelines and infrastructure as code tools.

Another contender is Spot, which seeks to optimize cloud infrastructure resources for reliability, security and efficiency at all times. It offers automation for virtual machines, containers and Kubernetes, a move designed to cut operational complexity and costs. With cloud management and governance tools, Spot can help you lock down resources, improve efficiency and get more out of your cloud budget.

Tanzu CloudHealth is a powerful platform for optimizing resource usage and cost control in multicloud environments. It offers personalized recommendations, resource management, budgeting and anomaly detection, among other features. Kubernetes optimization is a big part of the package, helping you monitor and right-size resources. It's used by many companies around the world to control cloud costs and improve operations.

If you prefer a more autonomous approach, Webscale uses AI and machine learning to optimize application delivery networks. Its Supercloud platform integrates with major cloud companies and automates workload orchestration and resource allocation. That can help you manage complex multicloud operations, improve app performance and cut cloud costs, making it a good fit for enterprise Kubernetes customers.