Free up to 650 hours a year with automated Kubernetes cost and performance optimization*
Automatically rightsizes Kubernetes workloads, cutting waste and freeing IT teams to focus on innovation, not manual tuning.
Dynamically scales pods based on SLO-driven metrics to keep apps responsive during demand spikes without costly overprovisioning.
Proactively moves pods across clusters to improve utilization, reduce idle capacity and maximize Kubernetes efficiency.
Generates and automates trusted actions that respect governance, licensing and security policies while keeping applications reliable.
Cuts expenses by eliminating overprovisioned resources while ensuring container workloads continue to perform without disruption.
Provides consistent workload optimization across Kubernetes distributions, including Red Hat® OpenShift®, and across multicluster environments.
Manage performance with Kubernetes optimization
Turbonomic ingests live metrics on CPU, memory and I/O consumption at the container, pod and node levels. It correlates this utilization data with requested and configured resources to identify inefficiencies. This continuous analysis forms the foundation for accurate optimization decisions across the cluster.
Turbonomic generates specific, executable actions such as adjusting CPU or memory requests, scaling replicas or rebalancing workloads. These actions are validated against performance, compliance and policy constraints before being recommended or automated. This engine moves beyond static monitoring to drive continuous, trusted optimization across Kubernetes environments.
Turbonomic integrates natively with the Kubernetes application programming interface (API) to execute optimization actions safely and seamlessly. By leveraging the control plane, it can apply rightsizing, scaling and scheduling adjustments without disrupting workloads. This tight integration ensures that optimization actions are transparent, auditable and aligned with cluster orchestration.
Every optimization action respects enterprise-defined policies, including affinity rules, licensing, governance and compliance constraints. Turbonomic’s automation framework ensures that resource adjustment and workload moves don’t violate operational requirements. This strategy creates a system of trust where optimization aligns with organizational standards by design.
Simulate optimization actions before execution to understand their impact on performance, capacity and cost. Turbonomic shows how resizing containers, moving workloads or adjusting nodes would impact cost, performance and capacity. By visualizing these outcomes in advance, teams can identify safe, cost-effective strategies and ensure that optimization aligns with SLOs and business goals.
Turbonomic ingests live metrics on CPU, memory and I/O consumption at the container, pod and node levels. It correlates this utilization data with requested and configured resources to identify inefficiencies. This continuous analysis forms the foundation for accurate optimization decisions across the cluster.
Turbonomic generates specific, executable actions such as adjusting CPU or memory requests, scaling replicas or rebalancing workloads. These actions are validated against performance, compliance and policy constraints before being recommended or automated. This engine moves beyond static monitoring to drive continuous, trusted optimization across Kubernetes environments.
Turbonomic integrates natively with the Kubernetes application programming interface (API) to execute optimization actions safely and seamlessly. By leveraging the control plane, it can apply rightsizing, scaling and scheduling adjustments without disrupting workloads. This tight integration ensures that optimization actions are transparent, auditable and aligned with cluster orchestration.
Every optimization action respects enterprise-defined policies, including affinity rules, licensing, governance and compliance constraints. Turbonomic’s automation framework ensures that resource adjustment and workload moves don’t violate operational requirements. This strategy creates a system of trust where optimization aligns with organizational standards by design.
Simulate optimization actions before execution to understand their impact on performance, capacity and cost. Turbonomic shows how resizing containers, moving workloads or adjusting nodes would impact cost, performance and capacity. By visualizing these outcomes in advance, teams can identify safe, cost-effective strategies and ensure that optimization aligns with SLOs and business goals.
Integrations
Turbonomic integrates with leading Kubernetes platforms such as Red Hat OpenShift, Amazon EKS, Azure AKS, Google GKE, IBM Cloud® Kubernetes and Kubecost®. It continuously monitors clusters and containers across hybrid and multicloud environments to recommend scaling and placement actions for optimal performance and efficiency.
Resources
Turbonomic starred in "Inside the Blueprint" on Bloomberg and FOX Business.
Turbonomic continuously rightsizes container requests and limits, consolidates pods and automates node actions. This approach eliminates overprovisioning and reduces Kubernetes expense while maintaining performance.
It analyzes real-time demand and scales pods through workload controllers to meet service level objectives (SLOs). By preventing congestion and under-resourcing, applications remain responsive even under peak load.
Most Kubernetes workloads request more CPU and memory than they use, inflating cloud bills. Turbonomic analyzes actual container usage and continuously compares it with configured requests and limits. It then generates safe, automated actions to rightsize workloads, ensuring reliability without waste.
HPA and VPA react to manually configured utilization thresholds and lack full stack context. They often overcorrect or miss efficiency opportunities. Turbonomic looks at demand, saturation, historical behavior across pods, nodes and clusters and constraints like namespace quotas, taints and tolerations, affinity and anti-affinity. It drives scaling decisions that maintain performance headroom while reducing unnecessary expense. Using a set of default policies to generate actions quickly, but are also configurable to suit your application needs.
Yes. Turbonomic supports Kubernetes distributions such as Amazon EKS, Azure AKS, Google GKE and Red Hat OpenShift. It provides consistent optimization across hybrid and multicluster environments.
No. Rightsizing, scaling and pod moves are executed safely through Kubernetes controllers. In the J.B. Hunt case, large-scale migrations and optimizations were completed with zero downtime.
Footnotes
*According to a J.B. Hunt case study conducted by IBM