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By Pepperdata
Kubernetes has revolutionized modern computing infrastructure, offering organizations near-infinite scalability, unparalleled agility in deploying new applications, and enhanced security. However, as enterprise cloud adoption continues to accelerate, that agility often comes with an unintended and costly side effect: skyrocketing cloud bills.
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By Pepperdata
To watch the full walkthrough video on the Pepperdata self-service install, click the link here. Many organizations struggle to efficiently manage their cloud costs, and that arises from difficulties in managing Kubernetes resources. Of the $419 billion spent on cloud infrastructure in 2025 (Synergy Research Group), Flexera estimates that 27% of all of cloud spend is wasted due to overprovisioned resources.
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By Pepperdata
Pepperdata announces launch of its Global Partner Program, a bold new initiative that brings together systems integrators, technology providers, and consultancies with Pepperdata's dynamic resource optimization platform for the cloud and on-premises environments.
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By Pepperdata
ATLANTA - November 10, 2025 - Pepperdata, the leader in Kubernetes resource optimization in the cloud and on prem, today announced the general availability of pepperdata.ai, a groundbreaking, automated optimization solution to reduce the cost of running AI workloads on GPUs. Attendees are invited to visit Pepperdata's booth at KubeCon and CloudNativeCon North America 2025 on November 11-13 at the Georgia World Congress Center in Atlanta, GA, to discover how Pepperdata helps organizations-including members of the Fortune Five-automatically maximize the efficiency and cost-effectiveness of their AI infrastructure and workloads.
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By Pepperdata
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.
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By Pepperdata
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.
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By Pepperdata
Running Kubernetes on AWS? You're probably using Karpenter, the open-source autoscaler that dynamically provisions new instances as your EKS workloads grow. Karpenter launches rightsized instances in real time in response to pending pods, based on available instance types and the resources applications need. It also terminates underutilized nodes to reduce costs.
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By Pepperdata
In this blog series we’re examining the Five Myths of Kubernetes Resource Optimization. The fifth and final myth in this series relates to another common assumption of many Kubernetes users: Dynamic Allocation for Apache Spark applications automatically prevents Spark from overprovisioning resources while improving workload utilization levels.
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By Pepperdata
In this blog series we’ve been examining the Five Myths of Kubernetes Resource Optimization. The fourth myth we’re considering relates to a common misunderstanding held by many Kubernetes practitioners: manual application tuning can increase resource utilization in my applications. Let’s dive into it.
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By Pepperdata
In this blog series we are examining the Five Myths of Kubernetes Resource Optimization. So far we’ve looked at Myth 1: Observability and Monitoring and Myth 2: Cluster Autoscaling. Stay tuned for the entire series! The third myth addresses another common assumption of many Kubernetes practitioners: Choosing the right instances will eliminate waste in a cluster.
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By Pepperdata
Installing kubernetes cost optimization software is typically time consuming and a drain on engineering teams. Pepperdata’s self-service install can get you started optimizing with just a few steps – completely on your own.
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By 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.
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By Pepperdata
Big data stacks are being moved to the cloud, enabling enterprises to get the most value from the information they own. But as demand for big data grows, enterprises must enhance the performance of their cloud assets. Faced with the complexity of cloud environments, most enterprises resort to scaling up their whole cloud infrastructure, adding more compute, and running more processes.
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By Pepperdata
There has been an ongoing surge of companies beginning to run Spark on Kubernetes. In our recently published 2021 Big Data on Kubernetes Report, we discovered that 63% of today's enterprises are running Spark on Kubernetes. The same report found that nearly 80% of organizations embrace Kubernetes to optimize the utilization of compute resources and reduce their cloud expenses. However, running Spark on Kubernetes is not without complications and problems.
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By Pepperdata
Increasingly, many organizations find that their current legacy monitoring solutions are no longer adequate in today's modern IT world. These enterprises find themselves struggling to manage and understand unprecedented amounts of data. With such large amounts of data needing to be dealt with, it is no wonder why it's a struggle for enterprises to leverage it for business success. Not to mention that optimizing performance and keeping costs in line is a technical challenge they must face at the same time.
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By Pepperdata
IT transformation projects are complex, demanding undertakings. They loop in multiple departments, and various budgetary considerations. This is a five-step guide designed to help enterprises and IT transformation teams prepare, plan, and execute their IT transformation strategy.
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By Pepperdata
According to Gartner, as of 2019, 35% of CIOs are decreasing their investment in their infrastructure and data center, while 33% of them are increasing their investments in cloud services or solutions.
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Pepperdata offers big data observability and automated optimization both in the cloud and on premises.
As big data stacks increase in scope and complexity, most data-driven organizations understand that automation and observability are necessary for modern real-time big data performance management. Without automation and observability, engineers and developers cannot optimize or ensure application and infrastructure performance, nor keep cost under control. With support for technologies including Kubernetes, Hadoop, EMR, GCP, Spark, Kafka, Tex, Hive, and more, Pepperdata knows big data. Powered by machine learning, the Pepperata solution delivers application SLAs required by the business while providing complete visibility and insight into your big data stack.
Pepperdata helps some of the most successful companies in the world manage their big data performance. These customers choose and trust Pepperdata because of three key product differentiators: autonomous optimization, full-stack observability, and cost optimization.
Automatically optimize your big data workloads in real time with these three key features:
- Autonomous Optimization: Pepperdata Capacity Optimizer provides autonomous optimization that enables you to reclaim resource waste with continuous tuning and automatic optimization; optimize Spark workloads with job-specific recommendations, insights, and alerts; and get an up to 50% throughput improvement to run more workloads.
- Full-Stack Observability: Pepperdata Platform Spotlight and Application Spotlight provide big data observability, giving you actionable data about your applications and infrastructure. Understanding system behavior can transform your organization from being reactive to proactive to predictive.
- Cost Optimization: Optimizing operational costs is critical for your business. As data volumes increase so does complexity—and the costs of processing it. Whether you are running Apache Spark, Hive, Kubernetes, or Presto workloads, Pepperdata can help your organization optimize operational costs.
Automatically Optimize Your Big Data Workloads and Control Costs on Any Cloud.