30% of Your Spend on Kubernetes is Wasted-Here's Why
30% of Your Spend on Kubernetes is Wasted—Here's Why.
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Optimizing GPU Efficiency and AI Costs with Pepperdata
As AI workloads explode, platform owners face an increasingly common challenge: a massive gap between GPU demand and supply. Pending workloads, idle GPUs, and rising costs make it harder than ever to scale AI efficiently. In this video, we explore how Pepperdata.ai helps enterprises regain control of their GPU environments with two breakthrough solutions: Demand Optimization – Get granular visibility into GPU usage across your entire infrastructure. Identify inefficiencies, balance supply and demand, and uncover hidden capacity.
The Solution to the Application Waste Problem
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How Pepperdata Increases Resource Utilization for Reduced Costs on Kubernetes | Pepperdata
Without Pepperdata, the Kubernetes system scheduler only sees resource allocations—causing low utilization and inflated spend. Watch the video to learn how Pepperdata optimizes workloads running on Kubernetes. Visit pepperdata.com to learn more.
Problems with Cloud Workload Observability
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How Pepperdata Differentiates from Other Optimizations
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How to Save on Cloud Costs with Ash Munshi
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Vertical Pod Autoscaling: How It Compares to Pepperdata Capacity Optimizer
Vertical Pod Autoscaling (VPA) is a component within Kubernetes designed to automatically resize the CPU and memory requests of pods based on their observed, historical usage patterns. While Pepperdata Capacity Optimizer and VPA both change the resource requests of pods in response to changing application resource requirements, there are several key differences.
Bonus Myth of Kubernetes Resource Optimization
In this blog series we’ve examined Five Myths of Apache Spark Optimization. But one final, bonus myth remains unaddressed: I’ve done everything I can. The overprovisioned resources that lead to underutilized CPU and memory in my workloads is just the cost of doing business.