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Wand leverages AI to continuously analyze, predict and adjust resource allocation in real-time, ensuring high utilization, performance and availability without risk or wasted resources.
Instead of wasting resources with over-provisioning or passive pod-level “right-sizing” (by peak load), we take a proactive multi-dimensional approach to auto-scaling. Wand dynamically foresees and adjusts resource use for workload demands with vertical, cluster and horizontal scaling. Combined.
No more static over-provisioning or wasteful reactive adjustments our solution predicts and adapts dynamically.
Workloads get exactly what they need, when they need it with multi-layered scaling for optimal results - whether it’s average use, bursts or low times.
No waste, no over-committing, no noisy neighbors—just optimized and high-performing infrastructure.
Some right-sizing solutions will passively adapt your resource use based on predefined peak usage or container limits. But the end-result is wasted resources and under-utilization.
Wand continuously and dynamically adapts resource allocation at the cluster level to meet demand while ensuring the highest level of utilization.
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